Scale matters: <scp>T</scp> he scale of environmental issues in corporate collective actions
Bibliographic record
Abstract
Research Summary: Much of the research on corporate collective action to manage common pool resources is focused on coordinated actions, such as voluntary programs, rather than collaborative actions, such as technology sharing. In this article, we examine inductively the collective actions taken by a consortium of 12 oil sands companies to address three environmental issues of different scale. We identified a set of organizing rules that determined whether the relationship among industry members would be collaborative or competitive, and found that the organizing rules for collaborative collective action were more effective for smaller scale issues (i.e., tailings ponds and water) than the larger scale issue (i.e., greenhouse gas emissions). Our findings contribute to research on the competitive dynamics of collaborating with competitors and on industry self‐regulation. Managerial Summary: Many environmental issues, such as climate change, water quality, and contaminated land, are caused by the overexploitation of commonly shared natural resources. Firms will often overuse resources because their cost of use is less than the benefit that accrues. In Alberta’s oil sands, 12 of the major oil sands operators, all competitors, have agreed to collaborate by sharing technology, which goes against the received wisdom of competition. This multiparty collaboration among competitors, while still relatively rare, is becoming increasingly commonplace. In this article, we outline the rules that allow this collaboration to flourish. Our most important finding is that the rules are shaped by the scale of the issue being managed, not the size of the collaboration.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".