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Record W4385867547 · doi:10.59962/9780774854108

Racing to the Bottom?

2007· book· en· W4385867547 on OpenAlexaboutno aff
Kathryn Harrison

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2007
Typebook
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsTop-down and bottom-up designComputer scienceProgramming language

Abstract

fetched live from OpenAlex

The spectre of a “race to the bottom” is increasingly prominent in debates about globalization and also within federal systems where the mobility of both capital and individuals prompts fears of interjurisdictional competition with respect to taxes and environmental and welfare standards. While there has been no shortage of either political rhetoric or academic theorizing on this subject, empirical studies have been in shorter supply. This volume seeks to fill that gap by asking: Are Canadian provinces engaged in a race to the bottom and, if so, what are the consequences? The contributors apply insights from economics and political science to several policy fields. What emerges is a theoretical and empirical picture of interprovincial competition that shows it to be more complex than the popular image of a race to the bottom and that also contradicts predictions of an inexorable downward spiral. This timely, practical volume will be of interest to public policy practitioners, as well as to students and scholars of economics and political science.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.470
Threshold uncertainty score0.934

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.010
Scholarly communication0.0100.006
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0190.004

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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2007
Admission routes1
Has abstractyes

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