Report on the Search Futures Workshop at ECIR 2024
Bibliographic record
Abstract
The First Search Futures Workshop, in conjunction with the Fourty-sixth European Conference on Information Retrieval (ECIR) 2024, looked into the future of search to ask questions such as: • How can we harness the power of generative AI to enhance, improve and re-imagine Information Retrieval (IR)? • What are the principles and fundamental rights that the field of Information Retrieval should strive to uphold? • How can we build trustworthy IR systems in light of Large Language Models and their ability to generate content at super human speeds? • What new applications and affordances does generative AI offer and enable, and can we go back to the future, and do what we only dreamed of previously? The workshop started with seventeen lightning talks from a diverse set speakers. Instead of conventional paper presentations, the lightning talks provided a rapid and concise overview of ideas, allowing speakers to share critical points or novel concepts quickly. This format was designed to encourage discussion and introduce a wide range of topics within a short period, thereby maximising the exchange of ideas and ensuring that participants could gain insights into various future search areas without the deep dive typically required in longer presentations. This report, co-authored by the workshop's organisers and its participants, summarises the talks and discussions. This report aims to provide the broader IR community with the insights and ideas discussed and debated during the workshop - and to provide a platform for future discussion. Date : 24 March 2024. Website : https://searchfutures.github.io/.
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.
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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.136 | 0.087 |
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".