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Record W3122423213

Co-operative Values, Institutions and Free Riding in Australia: Can it Learn from Canada?

2005· article· en· W3122423213 on OpenAlexaboutno aff
David Peetz

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsFree ridingCheatingAgency (philosophy)ObligationSolidarityIndividualismFree rider problemCollective actionState (computer science)BusinessPolitical scienceLaw and economicsEconomicsLawSociologyMarket economySocial psychologyMicroeconomicsIncentivePsychology
DOInot available

Abstract

fetched live from OpenAlex

Industrial citizenship implies both rights and responsibilities for citizens. These include the right to participate in collective activity such as bargaining, and the need to behave with responsibility towards fellow members of the collective or group. One of the central problems in co-operative, collective behaviour has been the problem of free riding. This paper will consider co-operative values, free riding and institutional change in the context of Australia, where the incidence of free riding has increased in recent years. It will also examine the contrast with Canada, where the Rand model for dealing with the free rider problem was introduced and briefly provided a model for emulation in Australia. I start by introducing the general context of collectivism and identify where co-operative values (including reciprocity and altruism) and free riding sit in a collectivist framework. I then look at the measurement of free riding and the general pattern of free riding in Australia. Consideration then turns to whether there is Australian evidence of a decline in cooperative values which might promote an increase in free riding, or whether institutional changes are responsible. I then look at how free riding has been dealt with or otherwise in the Canadian and Australian contexts, including some policy options for the future.

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 categoriesMeta-epidemiology (narrow)
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 score0.125
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
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.165
GPT teacher head0.357
Teacher spread0.192 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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