York University at TREC 2012: CrowdSourcing Track.
Bibliographic record
Abstract
The objective of this work is to address the challenges in managing and analyzing crowdsourcing in the information retrieval eld. In particular, we would like to answer the following questions: (1) how to control the quality of the workers when crowdsourcing? (2) How to design the interface such that the workers are willing to participate in and are driven to give useful feedback information? (3) How to make use the crowdsourcing information in the IR systems? The crowdsourcing system called CrowdFlower is employed and four classic information retrieval models are applied in our proposed approaches. Our experimental results show that the IR systems rene the results crowdsourcing by minimizing the manual work and the cost is much less.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.007 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.087 | 0.086 |
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".