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Record W3108855830 · doi:10.1787/750f13e6-en

Trade, investment and intangibles

2020· report· en· W3108855830 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOECD trade policy working papers · 2020
Typereport
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsMarketing buzzBusinessSocial connectednessAttractivenessValue (mathematics)Investment (military)Production (economics)Global value chainIndustrial organizationAppealEconomic geographyInternational tradeEconomicsComparative advantageAdvertising

Abstract

fetched live from OpenAlex

Located at the heart of global value chains (GVCs), intangibles are documented to have a high and rising value capture, and to depend on both agglomeration economies and global connectedness for their performance. In this paper, we study how the distinct nature of intangibles require countries to develop novel policy prescriptions to attract intangible-intensive activities and to increase the value capture of these activities. We suggest that such GVC-oriented policies fall into three categories: Attractiveness policies that aim to strengthen the appeal of a location for intangible activities; Buzz policies that intend to strengthen the local production and innovation ecosystem; and Connectedness policies that aspire to strengthen the local ecosystem's connections to other locations. Together, they constitute the ABCs of GVC-oriented policies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.092
GPT teacher head0.350
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it