“This is really interesting. I never even thought about this.” Methodological strategies for studying invisible information work.
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
Significant information work (Corbin & Strauss, 1985; 1988) is often required to grapple with increasing quantities, types, and sources of information. Much of this work is invisible both to researchers and to the people undertaking it. As there are many axes along which informational work can be made invisible, researchers require flexible and creative methods in order to bring hidden information work to light. Each drawing on our own information practices research study, we introduce and reflect on four methodological strategies that have been effective in recognizing and revealing hidden aspects of informational work: (1) consider the local and the translocal; (2) attend to the material and the textual; (3) consider visual methods; and (4) (re)consider the participant’s role and expertise. We conclude by reflecting on the benefits and pitfalls of bringing visibility to invisible information work and conclude with a call for further research focused on the invisible.
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 | 0.001 | 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 | 0.068 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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