Empirical Study on Identifying Collaborative Practices in Local Communities
Bibliographic record
Abstract
Our scientific approach addresses the issue of the economic collaboration of a community respectively the social economy enterprise. Our motivation is due to the profound transformations through which socio-economic activities pass over the last quarter of a century, more emphasized than ever by the particularities of the digital age. Social economy enterprises are also the subject to permanent adaptation to environmental conditions. Following the continuity of this process it was inevitable to avoid the following question, which has become the main objective of our paper: in an era where almost all processes and systems are digitized, leading to an increase in individualism, there still is availability for collaboration and if so, which are its defining factors? To answer this question we have initiated an exploratory analysis that allowed us to identify a number of defining factors of cooperation, each of them representing as many collaborative practices experienced in local communities. Analysis of data obtained as a result of the survey, conducted via questionnaire, was performed using IBM SPSS application. Interpretation of results is achieved by using optimal scaling technique known as categorical principal component analysis, CATPCA.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".