Reconsidering the dynamics of local knowledge creation: Middlegrounds and local innovation commons in the case of FabLabs
Bibliographic record
Abstract
Abstract This contribution investigates the dynamics of knowledge creation at the local level, building, in particular, on the interactions between formal and informal entities. Two theoretical constructions are brought together in order to do so: the middleground concept and the notion of commons. By associating these two concepts, the goal is to introduce a revised perspective on local dynamics of knowledge, which details how informal and formal entities interact with one another in order to drive local ideation processes and how these processes are structured in order to generate innovative outputs. The case of FabLabs is drawn upon in order to illustrate how the middleground and commons concepts can be mobilized to describe and better understand these local dynamics of knowledge creation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".