Bibliographic record
Abstract
The Caveat \n \nFor many reasons, names have had to be concealed within this document. The \nevents depicted are real and the discussions true. This is an attempt to legitimize the informal, seemingly mundane and sometimes personal: the author’s experiences bringing a folly to the physical, while trespassing into a new world: Islam. This thesis documents a series of interventions at different scales within that world. There is a book, the chair, and the city of Makkah. The events themselves are superimposed onto the traditional language, or professional conventions, used to justify them. Here, they are relegated to the margins of each page. This is akin to how some of the first books were produced, by students in the confines of dark cloisters or hot desert temples, struggling to maintain historical integrity while fighting the natural tendencies of youth. Their master’s voices always looking over the gutter from the opposite page. \n \nThe sketches for a new Makkah and a monumental demonstration in Canada unfold in parallel to a body of formal research. Together, as seemingly independently as they are, they paint the portrait of an Islam, while building a personality between the lines. \n \nThat being said: there isn’t a correct way to read it.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".