Bibliographic record
Abstract
ABSTRACT Ethnography is not a monolith but a shaggy extended family of research principles and tools that are continually evolving. Our methodological panel aims to explore this reality by illuminating the ethnographic variations that exist across the social sciences, with an eye to their applications in Information Science. The panelists are devotees of these lineages: visual ethnography , sensory ethnography , participatory forms of ethnography, digital ethnography ( netnography ) and multispecies ethnography . To help the audience grasp ethnography's heterogeneity, questions of a defining nature will be posed in turn to the speakers and each will supply a succinct answer from the perspective of their favored tributary. By design, the distinguishing features of each ethnographic variation will come into sharp relief. Following this structured and comparative exchange, a few interactivities will bring attendees into a free‐flowing conversation that enacts the “reflection, debate, and respectful balance” invoked in the Annual Meeting's theme. For example, we will brainstorm other shades of ethnography not yet mentioned; broach the matter of navigating methodological complexity; and air concerns about the shortage of methods training in these niches for students of Information Science. Overall, our panel aims to explore the diversity in play beneath the banner of ethnography and to elevate understanding of ethnography as a spacious, dynamic, multifarious yet welcoming methodological umbrella.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".