MétaCan
Menu
Back to cohort
Record W2073142998 · doi:10.1080/09548961003696120

Unintended consequences: examining the impact of tax credit programmes on work in the Canadian independent film and television production sector

2010· article· en· W2073142998 on OpenAlexaffabout
Amanda Coles

Bibliographic record

VenueCultural Trends · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProduction (economics)Unintended consequencesExcellenceWork (physics)BusinessCompetition (biology)Labour economicsVulnerability (computing)Tax creditEconomicsPublic economicsPolitical science

Abstract

fetched live from OpenAlex

In Canada, the institutional framework that shapes labour markets for film and television professionals includes a combination of policy instruments operating at local, provincial and federal levels. Through an examination of the impact that federal and provincial film and television tax credit programmes have on labour markets and workers in the English language Canadian independent film and television production industry, this analysis will demonstrate that despite having an overall, albeit fluctuating, positive impact on the volume of work at national and subnational levels, tax credit programmes at the provincial level are producing perverse labour market effects. Although designed to promote Canada and its provincial jurisdictions as globally competitive centres of excellence for film and television production, the application of the tax credit scheme has negatively impacted on both working conditions and labour mobility for highly skilled film and television production workers in the English language independent production sector. The end result is a policy regime deeply rooted in a competition framework that contributes to, rather than ameliorates, the vulnerability of workers in film and television production labour markets in both major and regional production centres.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.091
GPT teacher head0.335
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueCultural TrendsSame topicCultural Industries and Urban DevelopmentFrench-language works237,207