York University at TREC 2006: Enterprise Email Discussion Search
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
We use the Okapi retrieval system to conduct the email discussion search. The following issues are investigated. First, we make use of the thread structure in the emails to re-rank the documents retrieved by Okapi. We would like to see whether such post-processing of the retrieval result can boost the retrieval performance. Second, in terms of query formulation, we investigate whether the use of only title in a topic achieves better or worse results than the inclusion of other fields such as description and narrative. Third, we investigate whether stemming and stop word removal play an important role in the email search. Our conclusion includes that (1) re-ranking documents using a straightforward method that considers the thread structure can make a small improvement to the retrieval performance, (2) formulating the query using all the fields in a topic achieves the best result, and (3) the use of stemming and stop word removal can improve the performance, but the degree of improvement depends on the stemming method and the stop word list used.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it