On the Reusability of "Living Labs" Test Collections
Bibliographic record
Abstract
Information retrieval test collections are typically built using data from large-scale evaluations in international forums such as TREC, CLEF, and NTCIR. Previous validation studies on pool-based test collections for ad hoc retrieval have examined their reusability to accurately assess the effectiveness of systems that did not participate in the original evaluation. To our knowledge, the reusability of test collections derived from "living labs" evaluations, based on logs of user activity, has not been explored. In this paper, we performed a "leave-one-out" analysis of human judgment data derived from the TREC 2016 Real-Time Summarization Track and show that those judgments do not appear to be reusable. While this finding is limited to one specific evaluation, it does call into question the reusability of test collections built from living labs in general, and at the very least suggests the need for additional work in validating such experimental instruments.
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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 | 0.090 | 0.433 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".