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The Computer & Math 33%: Why the Most AI-Capable Occupation Group Still Automates Only a Third of Its Tasks

Posted on March 15, 2026March 19, 2026 by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19040207  30stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,521✓Minimum 2,000 words for a full research article. Current: 2,521
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19040207
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]14%✗≥80% of references from 2025–2026. Current: 14%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (16 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The Anthropic Economic Index (Massenkoff & McCrory, 2026) identifies computer and mathematical occupations as theoretically the most AI-exposed occupation group in the U.S. economy, with 94% of tasks rated as feasible for LLM acceleration. Yet observed automation covers only 33% of those tasks — producing a 61-percentage-point capability-adoption gap that is the largest absolute gap of any occu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19040207 30stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted25%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]2,521✓Minimum 2,000 words for a full research article. Current: 2,521
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19040207
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]14%✗≥80% of references from 2025–2026. Current: 14%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (16 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Frontier AI Consolidation Economics: Why the Big Get Bigger

Posted on March 15, 2026 by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19028157  27stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted13%○≥80% from verified, high-quality sources
[a]DOI4%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access8%○≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,169✓Minimum 2,000 words for a full research article. Current: 2,169
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19028157
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥80% of references from 2025–2026. Current: 58%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (11 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The frontier AI industry is consolidating at a pace that mirrors — and in some dimensions exceeds — the platform monopolization patterns of previous technology waves. As of early 2026, three providers control approximately 88% of enterprise AI API spending, with Anthropic commanding 40%, OpenAI 27%, and Google 21% of enterprise market share. Training costs for frontier models now exceed $100 mi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19028157 27stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted13%○≥80% from verified, high-quality sources
[a]DOI4%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed4%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access8%○≥80% are freely accessible
[r]References24 refs✓Minimum 10 references required
[w]Words [REQ]2,169✓Minimum 2,000 words for a full research article. Current: 2,169
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19028157
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥80% of references from 2025–2026. Current: 58%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (11 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Silicon War Economics: The Cost Structure of Chip Nationalism

Posted on March 14, 2026March 14, 2026 by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19021816  31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed3%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access9%○≥80% are freely accessible
[r]References33 refs✓Minimum 10 references required
[w]Words [REQ]2,506✓Minimum 2,000 words for a full research article. Current: 2,506
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19021816
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]47%✗≥80% of references from 2025–2026. Current: 47%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (17 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The global semiconductor industry, projected to reach $1 trillion in revenue by late 2026, has become the primary arena for a new form of economic warfare: chip nationalism. Nations are pouring hundreds of billions of dollars into domestic fabrication capacity, driven not by comparative advantage but by strategic anxiety. This paper examines the economic cost structure of semiconductor reshorin...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19021816 31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed3%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access9%○≥80% are freely accessible
[r]References33 refs✓Minimum 10 references required
[w]Words [REQ]2,506✓Minimum 2,000 words for a full research article. Current: 2,506
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19021816
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]47%✗≥80% of references from 2025–2026. Current: 47%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (17 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Enterprise AI Agents as the New Insider Threat: A Cost-Effectiveness Analysis of Autonomous Risk

Posted on March 14, 2026March 14, 2026 by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19019216  34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted32%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[f]Free Access26%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]3,124✓Minimum 2,000 words for a full research article. Current: 3,124
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19019216
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✗≥80% of references from 2025–2026. Current: 71%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid deployment of autonomous AI agents across enterprise environments has introduced a novel category of insider threat that traditional cybersecurity frameworks are ill-equipped to address. According to the Thales 2026 Data Threat Report, 61% of organizations now cite AI as their top data security concern, while only 34% maintain visibility into where all their data resides. This article...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19019216 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted32%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[f]Free Access26%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]3,124✓Minimum 2,000 words for a full research article. Current: 3,124
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19019216
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]71%✗≥80% of references from 2025–2026. Current: 71%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Policy Implications and a Decision Framework for Shadow Economy Reduction in Ukraine

Posted on March 14, 2026March 14, 2026 by
Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19016590  59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed86%✓≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,069✓Minimum 2,000 words for a full research article. Current: 2,069
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19016590
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]14%✗≥80% of references from 2025–2026. Current: 14%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Paper 3 of 3 in the series "Shadow Economy Dynamics." Builds on Paper 1: Problem Landscape and Paper 2: Scenario Analysis.

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19016590 59stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed86%✓≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,069✓Minimum 2,000 words for a full research article. Current: 2,069
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19016590
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]14%✗≥80% of references from 2025–2026. Current: 14%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (64 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Scenario Analysis: Modeling Three Futures for Ukraine’s Shadow Economy (2025–2030)

Posted on March 14, 2026March 14, 2026 by
Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19016584  51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic22%○≥80% from journals/conferences/preprints
[f]Free Access78%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]2,248✓Minimum 2,000 words for a full research article. Current: 2,248
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19016584
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]22%✗≥80% of references from 2025–2026. Current: 22%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In Paper 1 of this series (Ivchenko, Ivchenko & Grybeniuk, 2026a), we established that Ukraine's shadow economy has remained persistently high — between 30% and 45% of official GDP over the decade 2015–2025. We identified two competing feedback loops: a reinforcing cycle where high tax burdens push economic actors into informality, and a balancing mechanism where digitalization increases tr...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19016584 51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic22%○≥80% from journals/conferences/preprints
[f]Free Access78%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]2,248✓Minimum 2,000 words for a full research article. Current: 2,248
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19016584
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]22%✗≥80% of references from 2025–2026. Current: 22%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (50 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Legal 15%: Liability Is Not a Technical Problem

Posted on March 14, 2026March 14, 2026 by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19015448  40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access27%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,105✓Minimum 2,000 words for a full research article. Current: 2,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19015448
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]83%✓≥80% of references from 2025–2026. Current: 83%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The Anthropic Economic Index (Massenkoff & McCrory, 2026) reveals a persistent and structurally significant anomaly: legal occupations exhibit only 15% observed AI exposure despite theoretical automation potential that rivals software engineering. This article examines the economic architecture of that gap. Unlike healthcare, where clinical decision liability and FDA approval pathways create te...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19015448 40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI13%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access27%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,105✓Minimum 2,000 words for a full research article. Current: 2,105
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19015448
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]83%✓≥80% of references from 2025–2026. Current: 83%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Tax Burden, Digitalization, and Shadow Economy in Ukraine: A Problem Landscape (2015–2025)

Posted on March 13, 2026March 14, 2026 by
Economic Research
Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk  ·  DOI: 10.5281/zenodo.19008827  45stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef13%○≥80% indexed in CrossRef
[i]Indexed27%○≥80% have metadata indexed
[l]Academic27%○≥80% from journals/conferences/preprints
[f]Free Access47%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,015✓Minimum 2,000 words for a full research article. Current: 2,015
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19008827
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (41 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Ukraine's shadow economy remains one of the largest in Europe, consistently estimated at 30–45% of official GDP over the past decade. This phenomenon directly undermines fiscal stability, distorts market competition, and complicates the country's path toward EU accession. The interaction between tax policy, digital governance, and informal economic activity forms a complex adaptive system where...

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Economic Research by Oleh Ivchenko, Iryna Ivchenko & Dmytro Grybeniuk DOI: 10.5281/zenodo.19008827 45stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI40%○≥80% have a Digital Object Identifier
[b]CrossRef13%○≥80% indexed in CrossRef
[i]Indexed27%○≥80% have metadata indexed
[l]Academic27%○≥80% from journals/conferences/preprints
[f]Free Access47%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,015✓Minimum 2,000 words for a full research article. Current: 2,015
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19008827
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (41 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Measurement Crisis: Saturation, Goodhart’s Law, and the End of AI Leaderboards

Posted on March 13, 2026March 13, 2026 by
Benchmark Research
Benchmark Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19007432  48stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]3,049✓Minimum 2,000 words for a full research article. Current: 3,049
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19007432
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥80% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The AI evaluation ecosystem is in crisis. Frontier models now exceed 90% accuracy on MMLU, 95% on HumanEval, and 93% on HellaSwag — scores that were considered unattainable three years ago. This saturation is not evidence of intelligence; it is evidence that our instruments have failed. We argue that three convergent forces have rendered current AI leaderboards meaningless: (1) benchmark satura...

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Benchmark Research by Oleh Ivchenko DOI: 10.5281/zenodo.19007432 48stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI67%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed33%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]3,049✓Minimum 2,000 words for a full research article. Current: 3,049
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19007432
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥80% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (45 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Buy vs Build in 2026: Why CIOs Are Choosing Integrated Agentic Ecosystems

Posted on March 13, 2026 by
Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19005352  38stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources9%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI18%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed36%○≥80% have metadata indexed
[l]Academic9%○≥80% from journals/conferences/preprints
[f]Free Access27%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,029✓Minimum 2,000 words for a full research article. Current: 2,029
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19005352
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]78%✗≥80% of references from 2025–2026. Current: 78%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The classic "build vs buy" dilemma in enterprise software has been resolved for most AI deployments in 2026 — not by a clear winner, but by a third option that renders the original question obsolete. As Gartner projects worldwide AI spending at $2.5 trillion in 2026, enterprises are abandoning bespoke AI moonshots in favour of orchestrated integration across incumbent vendor ecosystems. This ar...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.19005352 38stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources9%○≥80% from editorially reviewed sources
[t]Trusted36%○≥80% from verified, high-quality sources
[a]DOI18%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed36%○≥80% have metadata indexed
[l]Academic9%○≥80% from journals/conferences/preprints
[f]Free Access27%○≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,029✓Minimum 2,000 words for a full research article. Current: 2,029
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19005352
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]78%✗≥80% of references from 2025–2026. Current: 78%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Cost-Effective Ent…Read More
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