Undoing decontextualization or how scientists come to understand their own data/graphs
Bibliographic record
Abstract
ABSTRACT The sciences have been so successful in the course of recent human history because the (mathematical) representations they use articulate laws and relations independent of contextual particulars and contingencies of concrete situations. This allows verification anywhere and at any time, and, therefore, the objectivity of scientific phenomena. Decontextualization, however, may make interpretation difficult even for scientists. This ethnographic study of a scientific lab investigating the absorption of light in the eyes of salmonid fish was designed to investigate the role of context in the understanding of data and graphs in science. Drawing on data from a 5‐year ethnographic study of laboratory science, I exhibit the effort scientists mobilize to learn by reconstructing the context from which their data have been abstracted. Without recontextualization, scientists struggle making sense of the study results that emerge from their work. Scientists require familiarity with the settings from which the data derive and with the entire transformation process that produce graphical representations to be able to interpret the data. This has considerable implications for teaching graphs and graphing and for using graph interpretation tasks. Rather than being a decontextualized basic process skill, graphing competency is a function of familiarity with both scientific object and the research process as a whole.
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 | 0.025 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".