Report on the 1st Early Career Researchers' Roundtable for Information Access Research (ECRs4IR 2022) at CHIIR 2022
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
The First Early Career Researchers Roundtable for Information Access Research Workshop , in conjunction with the Seventh ACM Conference on Human Information Interaction and Retrieval (CHIIR) 2022, looked into the future of research, collaborations, and self-development to ask the following. Where are the opportunities for researchers in a (post-)pandemic environment, especially for Early Career Researchers (ECRs)? What do we need to do to get there? Which practical implementations can the broader CHIIR community support? The workshop started with an invited talk. Instead of conventional paper presentations, the attendees discussed the lessons learned from working in a pandemic. This report, co-authored by the workshop's organisers and its participants, summarises the discussion. This report aims to provide the broader CHIIR community with feedback on the workshop and foster ideas raised by ECRs to support ECRs. Two primary outcomes are (i) ECRs are often enthusiastic about taking on roles within a community, but formal validation and recognition are needed for their efforts and (ii) that the role of a conference needs to be reevaluated optimising the benefits of attending the event. Date: 14 March 2022. Website: https://sites.google.com/view/ecrs4ir/home.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.007 |
| 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