Ethnographic Stories in Information Science
Bibliographic record
Abstract
ABSTRACT Considering the conference theme “Putting People First: Responsibility, Reciprocity, and Care in Information Science Research and Practice,” this panel brings an ethnographic methodological conversation to the 2024 ASIS&T Annual Meeting. Our session emphasizes how participants' stories are one of the most human‐centered tools we have in research, highlighting how storytelling is an integral part of being human. The panelists have conducted ethnographic fieldwork in various contexts and begins with an introduction about ethnography as a form of storytelling, introducing concepts of vulnerability and reciprocity. Panelists will then reflect on ethnographic stories before turning to teaching Information Ethnography. Our session aims to broach the joys and challenges of ethnographic research by bringing a new honesty to the conversation in Information Science. We will engage the audience in open discussion, before breaking out into smaller groups, fostering an intimate, safe space to share stories about past research.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".