Sectoral Strategies in CED: Critical factors in the success of CHCA & Childspace
Bibliographic record
Abstract
Scale and markets notwithstanding, Cooperative Home Care Associates (New York City) and Childspace Day Care Centers (Philadelphia) have plenty to teach Canada's co-operators and other CED activists. Kreiner challenges CED practitioners to move beyond creating business just within ‘their’ territory; they are often poor, thus possess weak markets and often have a hard time attracting entrepreneurial and professional management. He asserts one can be often more strategic and get better results through focusing on sectors where the quality of service provided is a competitive advantage. The twin benefit of better jobs (within the sectors targeted) and higher quality service (through workers also being owners) is backed up by the evidence presented in these two cases. Interestingly, the cases he presents both are both ‘human services’. Later in this volume, John Restakis writes about the Emilian model in northern Italy, where social care co-operatives have rapidly expanded into a wide range of social and health services that now dominate the region (57). His conclusion is similar to Kreiner; where social ownership and participation in management exists higher quality services result and a competitive advantage in the market is achieved.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".