Pulling back the curtain on conducting social impact research
Bibliographic record
Abstract
Abstract Conducting research that has social impact is more than simply disseminating research when it is completed. It is more than looking at the influence research has on a research field or discipline. Conducting research that has social impact is a process of engaging communities in the research process and ensuring that the communities that take part in the research experience benefits. Socially impactful research is messy and challenging. It takes commitment from researchers to consider social impact and integrate practices into the entire research process from planning to collecting data, to communicating and implementing findings with communities. While this research is taking place within the field of information behavior/information practices, many of the ways this research is carried out are hidden. In this panel, four information behavior/information practice researchers will discuss research projects that have social impact and “pull back the curtain” on their approach to this research, what this means practically for carrying out this research, as well as how research findings are communicated and applied.
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 | 0.394 | 0.667 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.026 | 0.101 |
| Scholarly communication | 0.048 | 0.049 |
| Open science | 0.007 | 0.040 |
| Research integrity | 0.019 | 0.056 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".