Self-Assessment and Strategic Planning in the Retail Co-op Food Sector: Using the Sustainability and Planning Scorecard Kit (SPSK) in a Crisis Context
Bibliographic record
Abstract
After the 2015 collapse of Co-op Atlantic, the second-tier co-operative providing services and support to retail co-operatives in Canada’s Atlantic Provinces, many co-operatives contracted with Sobeys to provide services under the Foodland banner. The co-operatives’ ongoing challenge is to reaffirm their place in the community and their co-operative identity, contributing to ongoing innovation around a co-operative model appropriate to these new circumstances. This article analyzes the strategic planning process undertaken by a small PEI Foodland co-operative as it defined new directions during this crisis. It also examines the co-operative’s use of a collaboratively developed Sustainability and Planning Scorecard Toolkit. The toolkit was designed to help co-operatives through their self-evaluation process, while also facilitating the further steps of planning and taking action.RÉSUMÉSuite au démantèlement de Coop Atlantique en 2015, la coopérative de second niveau qui offrait des services et du soutien aux coopératives de consommation dans les provinces atlantiques, plusieurs coopératives ont négocié une entente d’approvisionnement avec Sobeys, sous la bannière Foodland. Ces coopératives doivent réaffirmer leur place dans la communauté et leur distinction coopérative, et contribuer au développement d’un modèle coopératif innovant et adapté à cette nouvelle réalité. Cette recherche analyse le processus de planification stratégique ayant permis à une petite coopérative de l’Î.-P.-É. de relever ce défi dans un contexte de crise. Nous étudions aussi l’utilisation par la coopérative de l’Outil d’évaluation et de planification du développement coopératif durable, outil développé de façon collaborative. Cet outil a été conçu pour appuyer les coopératives dans une démarche d’auto-évaluation, tout en leur permettant de poursuivre les différentes étapes de conception et de mise en oeuvre d’un plan stratégique.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".