Co-operative Values, Institutions and Free Riding in Australia: Can it Learn from Canada?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".