Interpreting mediated quantitative data: Insights from information behaviour research
Bibliographic record
Abstract
Using climate change as an example, this conceptual paper explores two issues: the difficulties people have in understanding, interpreting, and responding to quantitative data, and the ways in which, if any, information behaviour research might provide insight into this issue. Data related to climate change were selected because they are often mediated by others with divergent vested interests including media, politicians, NGOs, scientists, and government agencies and because people bring a host of cognitive and psychological biases, worldviews, and beliefs to their perceptions of data and information and consequently, climate change.En utilisant le changement climatique comme exemple, cet article conceptuel explore deux questions : les difficultés rencontrées par les personnes pour comprendre, interpréter et répondre aux données quantitatives, et la manière dont, le cas échéant, la recherche sur le comportement informationnel pourrait éclairer cette question. Les données relatives au changement climatique ont été choisies car elles sont souvent véhiculées par des groupes ayant des intérêts divergents, notamment les médias, les politiciens, les ONG, les scientifiques et les agences gouvernementales, et parce que les gens apportent une foule de préjugés cognitifs et psychologiques, de visions du monde et de croyances dans leur perception des données et des informations, et donc du changement climatique.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.015 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".