Words Matter: Measuring the Co-Operative Identity Crisis
Bibliographic record
Abstract
Identity is at the core of a rich body of business communications research, spanning studies on organizational identity, branding, and corporate social responsibility. This work has, however, neglected the question of corporate identity from the perspective of co-operatives—democratically-controlled businesses owned and controlled by their users—and the existential challenge posed by an operating environment often hostile to the business model. At the same time, the question of identity permeates the scholarly organizational and co-operative literature, shaping studies into co-operative identity crises, isomorphism, and from a transactions-cost economics perspective, the co-operative lifecycle. Bridging these literatures, we develop a first-ever conceptual dictionary of terms that we associate with co-operative and investor-owned firms (IOFs). Using text-as-data techniques, we apply the dictionary to a 15-year sample of credit union (a type of co-operative) and bank (IOFs) annual report texts. The resulting model ranks credit unions and banks on a co-op versus IOF firm scale and identifies credit unions that may be at risk of losing their identity because of their use of IOF language. To validate our results, we employ a variety of strategies, including novel machine learning models. Generally, these strategies support the findings from our dictionary model but also suggest the model may not be picking up on some creeping isomorphic pressures on credit unions to conform to IOF language. We conclude by noting that identity questions have important real-world implications, noting potential legal and public policy implications (e.g., loss of preferential tax measures) and pointing to literature that associates co-operative “identity crises” with business failures and demutualizations which, in turn, can lead to higher consumer prices.
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 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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".