Connecting with Information Sources: How Accounts of Information Seeking Take Discursive Action
Bibliographic record
Abstract
Taking a discursive approach to information seeking research can allow researchers to move away from considering information seekers’ accounts as transparent and unproblematic representations their information behaviour or underlying cognitive and affective processes. This paper uses a constructivist discourse analytic approach to study the discursive functions performed by accounts of information seeking in a particular context – the ways that individuals use information-seeking stories to “position” themselves discursively. This paper analyses four modes of information practice (Active seeking, Active scanning, Everyday monitoring, Information seeking by proxy) present in participants’ accounts of connecting with information sources. Although the four modes represent varying levels of active engagement in information seeking, accounts of the four modes fulfilled the same discursive function of demonstrating the individual to be an autonomous actor, someone who is active, connected, attentive to the environment, alert to unexpected possibilities, and receptive to appropriate forms of information. Working from a constructivist discourse analytic approach allows the researcher to attend both to the characteristics of the information-seeking context (in this case, pregnancy) and to the researcher-participant interaction and the functions that accounts of information seeking perform within it. It is then possible to study the ways that accounts of information seeking may themselves take discursive action.
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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 | 0.034 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.032 | 0.058 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".