Brenda Dervin's Sense‐Making Methodology: What Has Been Achieved and Why It Matters Now?
Bibliographic record
Abstract
ABSTRACT Brenda Dervin made a tremendous contribution to both the fields of communication and information science through her Sense‐Making Methodology. She was one of the first to advocate for a user‐centered perspective in the field and had a tremendous impact on generations of researchers across various disciplines. Almost three years after her passing in December 2022, this panel brings together a diverse range of speakers to celebrate Dervin's contribution to ASIS&T and to information science. This highly interactive panel will cover various topics ranging from interactions with Dervin to the use of Dervin's SMM in theory, research, practice, human interaction, and artificial intelligence tools. The panel members have either engaged with Dervin the person, with her Sense‐Making Methodology, or both. The session hopes to inspire the audience to use Dervin's Sense‐Making Methodology (SMM) in their research and practice.
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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 | 0.088 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".