Volunteering – An Efficient Collaborative Practice for the Local Communities Sustainability. Empirical Study
Bibliographic record
Abstract
The scientific research we have conducted concerns volunteering as a collaborative practice and as a basis for the sustainable development of local communities. Identifying the suitable practices capable of transforming a group of individuals into a prosperous and sustainable collaborative community has became our priority concern. Therefore, the main objective of the study was outlined as a response to the question: “which are the tangible and intangible effects of the acts and facts of the collaboration identified in the communities where the participants in the study came from?”. The answer was prefigured as the outcome of an exploratory analysis that also revealed to us the motivation, satisfaction and results obtained by the participants in the survey, as a consequence of their personal experience in relation to their community. The quantitative analysis of collected data was performed in IBM SPSS software and the qualitative analysis with the Atlas Ti application. Despite the poverty of information sources in the field, our exploratory research has succeeded in highlighting the role of volunteering as a factor of sustainable social cohesion and practice in local communities. And this is at least one of our reasons useful to continue our theoretical and applied researches related to the emergence and sustainable development of collaborative communities.Keywords: exploratory analysis; collaborative community; sustainability; social economy enterprise; social innovation
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".