Bar Camps: Reflections on the Ideology of Participation and the Politics of Knowledge Production
Bibliographic record
Abstract
In close association with online cultures based on the ideals of non-proprietysharing, a series of experiments in face-to-face participatory knowledgeproduction have emerged under the name of “bar camps.” Also referred to asunconferences, bar camps are “user-generated” conferences or workshopsmodeled on the participatory structures used in Free Libre Open SourceSoftware (FLOSS) production. The first bar camp took place in 2005 and wasorganized by IT workers in reaction to the inaccessible, non-participatorynature of a major IT conference which they desired to attend but could notdue to limited space and restrictive fees. The initiators of this first bar campwere critical of conferences that featured famous lecturers and argued that thismodel of knowledge exchange leaves the brain power of the audience untappedand therefore wasted. In contrast, the FLOSS method of working encouragesthe contributions of the audience within an open structure and is perceived tolead to better, more efficient solutions. In the following I focus on a groupcalling themselves Critical Practice (CP), a group who privilege participatorystructures like bar camps as sites of knowledge production within thehumanities. CP offer an example of the influence of FLOSS culture oneducational and artistic spaces, and exhibit an ideology of participation that Iargue may be more damaging than helpful to critical thinking.
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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 | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.105 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.013 | 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".