Self-managed and cooperative processes: Knowledge construction, interactional spaces, expanding territories
Bibliographic record
Abstract
In this paper I will address issues regarding self-management (auto-gestión) and cooperative processes as they relate to educational experiences currently taking place in Argentina. A first note of caution is that in Spanish, the meaning of auto-gestión cannot easily translate into English as self-management. I will use this category, though, as a placeholder and for lack of a better word, but we should keep in mind that here self-management refers to a kind of organizational structure AND an ideological perspective based on a specific type of authority (collective), which entails specific decision making processes based on internal democracy, on solidarity and cooperative orientations and on a strong critique over capitalism as an economic and ideological system. Therefore, when I use the term self-management I am concurrently referring to processes of self-organization (self-government) and self-management, which are both and at the same time, political and educational. This perspective has a long tradition in Latin America, and it is usually referred to as educación popular. In other parts of the world, e.g., in the United Sates and Canada, it is also referred to as critical education, liberatory education, critical pedagogy or emancipatory education. These perspectives build on work conducted by Latin American, North American, African, Australian and European scholars framed in post-modern, post-colonial and de-colonial perspectives (e.g., De Souza Santos; Mignolo; Giroux; McLaren; etc.).For the past seven years, I have been documenting the work of several different types of self-managed organizations, e.g., cooperatives of workers, volunteer groups, non-governmental organizations, self-managed groups within public, state-funded organizations. I have identified and analyzed these organizations? learning processes on two different yet related areas: that of their organization and sef-government; and that of their specific area (e.g., education; homeless housing projects; performance and arts; music; food production; printing; etc.).In this presentation I will focus on organizations whose direct area is conceived of as educational or school-related, and I will base my analysis on my own primary data and data reported by other researchers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".