Bibliography of the Papers from the International Conferences on Sino-Tibetan Languages and Linguistics I-XXV (second edition)
Bibliographic record
Abstract
The annual conferences on Sino-Tibetan languages and linguistics began on a small scale at Yale in 1968, with only eight conferees sitting around a table, but have grown tremendously over the years, until they now usually attract over 100 participants, and have become the chief focus of scholarly activity in the field. Ever since 1971, the word “international” has appeared in the official title of the Conferences, and rightly so, since they have become truly global in scope. Since the mid-1970’s, they have increasingly been held outside the U.S.: Copenhagen (1976), Paris (1979), Beijing (1982), Bangkok (1985), Vancouver (1987), Lund (1988), Bangkok (1991), Osaka (1993), Paris (1994) [planned]. [...] Most of the papers presented at the Conferences are of high quality, and usually find their way into print within a few years. Yet in spite of valiant attempts to put out real volumes of Proceedings, e.g. the partial collection achieved for #14 (University of Florida, 1983), the most that has been managed is a photocopied version of the papers velo-bound together (e.g. for #16, University of Washington, 1983), or a collection of the abstracts submitted by the participants, e.g. for #15 (Beijing, 1982), for #18 (Ramkhamhaeng University, Bangkok, 1985), or for #25 (University of California, Berkeley, 1992). It was realized early on that it would be a good thing to keep some kind of record of which papers were presented when, before things got too badly out of hand. [...] The first version of this Bibliography (1989) was produced with admirable thoroughness and rapidity by the members of the STEDT staff. John B. Lowe devised the Macintosh software for the job, and the inputting of the authors and titles was done by many willing hands. Randy J. LaPolla did most of the editorial work on the first edition: he translated the dozens of Chinese titles, tracked down almost all the published versions of the papers by scouring journals and bibliographies, and wrote personally to many authors requesting addenda and corrigenda to the listings of their works.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.266 | 0.173 |
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".