“This is really interesting. I never even thought about this.” Methodological strategies for studying invisible information 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.
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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 | 0.042 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".