Cooperative associations: frameworks of distributed leadership for collective digital innovation
Bibliographic record
Abstract
Digital innovation and transformation have been mainly studied at the level of a single organization: in the cooperative movement, platform cooperative have triggered a lot of attention, with exciting studies about the potentials and limitations about how a digital platform can integrate into a cooperative governance. However, digital innovation at the level of a group of cooperatives, such as automated data-sharing systems, has remained (to our knowledge) unexplored, leaving scholars and practitioners with poor knowledge and material to support a large-scale digital transition of the cooperative movement. This paper aims at contributing to fill this gap by studying how a group of cooperatives or social economy organizations can collectively take leadership over a digital project within the framework of a meta-organization more commonly known as a cooperative association or umbrella organization. To do so, we shaped a theoretical model mobilizing the emerging theory of meta-organizations (Ahrne & Brunsson, 2005) together with concept of distributed leadership (Huxham & Vangen, 2000), and conducted a preliminary qualitative study based on two cases in Quebec. This enables us to identify configurations preventing or supporting the emergence of collective digital projects, by taking into account characteristics of both the meta-organization and its members. We believe that such findings could open a new stream of research on cooperatives adopting interorganizational collaboration as a unit of analysis, and help cooperative practitioners in conducting complex data-sharing innovations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".