Introduction to Special Issue on Third North American Anarchist Studies Network Conference
Bibliographic record
Abstract
To those who were unable to attend the Third Annual Conference of the North American Anarchist Studies Network this past January in San Juan, Puerto Rico, let me take this opportunity to express my sincere condolences.To say you missed out would be an understatement of North American (not Caribbean, I hasten to add!) proportions.Compared to its predecessors, NAASN-3 2 was a much more relaxed and intimate affair than its predecessors, which is perhaps appropriate enough for an anarchist gathering on a tropical island.True, it was not nearly as well-attended as the spectacular 2009-10 inaugural in Hartford, let alone the larger-than-life anarcho-extravaganza that was NAASN-2 (2011-2012) in Toronto, but what NAASN-3 lacked in quantity it more than made up for in prolific je ne sais quoi (or should I say no se que?).With the exception of a few familiar faces-from the elder statesmen (Dana Ward, Allan Antliff, Jon Bekken), to the stalwarts (Jesse Cohn, Andrew Hoyt, Michael Loadenthal, Ed Avery-Natale, James Birmingham), to the diplomats from abroad (Alex Prichard)-the attendees were overwhelming Puerto Rican (with a random Swiss thrown in here and there for good measure! 3 ).This was true not only of the presenters and conference organizers, but also of the people in the audience-many of whom, by all reasonable appearances, were neither anarchists nor even radicals, but simply ordinary Puerto Ricans who had 1
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.511 | 0.272 |
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".