SIG CON Research Symposium: [Insert Title Here: Make Sure to Satisfy Titular Colonicity]
Bibliographic record
Abstract
EDITOR'S SUMMARY Bringing comic relief to the 2015 ASIS&T Annual Meeting, SIG CON opened with discovery that the alleged nephew of the group's figurehead, Dr. Llewellyn C. Puppybreath III, and speaker at the 2014 meeting was an imposter of dubious character. With the purloined ceremonial wand accounted for, the 2015 symposium opened with a presentation on the I‐Index, an anti‐establishment altmetric, eschewing group recognition in favor of individualism and self‐citation. A paper on the correlation among computer science doctorates, rising arcade revenues and climate warming in Australia highlighted the income and career opportunities available to techies relocating to Oz. Analysis of ASIS&T members' social media posts revealed a skewed distribution of posters, topics and irrelevant content, especially by Association leadership. The session also featured a Monty Python character reporting on fatal answers at the Bridge of Death, multiple personalities of Dr. N. E. Doofus and a séance with illustrious ASIS&T members spanning the spectrum from living to dead.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.368 | 0.400 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".