Bibliographic record
Abstract
By engaging in a close reading of the work of @snappydragon and @sexycyborg, “Crafting Intellectual Community Online” explores how they assert a personal identity that is strongly tied to their embodied experiences as women, each with distinct interests and positionalities: one a Jewish woman interested in historical clothing and identifying dress, and the other a Chinese woman interested in cyberpunk and augmentation. In addition to the thematic links in their work, both assert their personal agency by explaining their making process in ways that showcase the conceptual considerations of the project through to the material execution, inviting viewers to engage in similar considerations by taking up a making practice of their own. By placing their work in dialogue, “Crafting Intellectual Community Online” argues for an intellectual community that develops outside the academy and enables collaborative thought across physical and temporal distance. My work thinks through these invitations in relation to the theoretical work of Hannah Arendt and Paulo Friere, drawing on ideas related to the formation of an intellectual community of peers through a dialogic method which can then be reinterpreted in relation to the unique challenges of the internet as a new site of education beyond the academy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".