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Record W4207001382 · doi:10.1111/1911-3846.12758

Regulatory Approval and Biotechnology Product Disclosures*<sup>†</sup>

2022· article· en· W4207001382 on OpenAlexafffundvenue
Luminiţa Enache, Lynn Li, Edward J. Riedl

Bibliographic record

VenueContemporary Accounting Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Calgary
FundersChartered Professional Accountants of Canada
KeywordsProduct (mathematics)New product developmentBusinessIncentiveRevenueRegulatory focus theoryIndustrial organizationBiotechnologyCapital (architecture)Voluntary disclosureAccountingMarketingEconomicsMicroeconomicsBiologyManagement

Abstract

fetched live from OpenAlex

ABSTRACT This study examines the effect of regulatory approval on a firm's voluntary product‐level disclosures. We focus on the US biotechnology industry, a setting that allows direct observation of whether firms disclose more information as products proceed through well‐defined—though successively more complex and costly—regulatory hurdles. Consistent with predictions motivated by biotech firms' need to repeatedly raise capital, we find that firms disclose more as their products move to later stages in the development process, both when the products receive regulatory approvals as well as when they receive regulatory denials. In addition, these findings are consistent across phases of development as well as product disclosure categories and are accentuated for firms without internal sources of capital (i.e., lacking product revenue). Collectively, these findings reveal that biotechnology firms respond to the considerable incentives to provide enhanced product disclosure and thus facilitate their ongoing need for capital to proceed through subsequent stages of product development.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.282
Teacher spread0.126 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes3
Has abstractyes

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