The First Workshop on Evaluation Methodologies, Testbeds and Community for Information Access Research (EMTCIR 2024)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evaluation campaigns, where researchers share important tasks, collaboratively develop test collections, and have discussion to advance technologies, are still important events to strategically address core challenges in information access research. The goal of this workshop is to discuss information access tasks that are worth addressing as a community, share new resources and evaluation methodologies, and encourage researchers to ultimately propose new evaluation campaigns in NTCIR, TREC, CLEF, FIRE, etc. The proposed workshop accepts four types of contributions, namely, emerging task, ongoing task, resource, and evaluation papers. The workshop will start with presentation of accepted papers and introduction of ongoing tasks. The rest of the workshop will be run in an interactive manner: round-table discussion on potential tasks.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it