Information creation and the ideological code of “keeping track”
Bibliographic record
Abstract
Introduction. This paper considers the practices of information creation in personal information management by studying the work of keeping track in everyday life, e.g., creating lists and calendars.Method. We interviewed ten participants from two Canadian provinces about how they keep track and we observed and photographed the physical spaces and the documents they created and used. Our data set consists of fourteen hours of interviews, 330 photographs and 500 pages of interview transcripts.Analysis. We used the qualitative technique of constant comparison within an abductive framework of relational and discourse analysis to study a) how the domestic work of keeping track hooks into the requirements of organizations such as schools and workplaces, and b) how talk about keeping track relates to participants' presentations of themselves as good workers, parents, citizens, etc.Results. The work of keeping track functions in terms of Dorothy Smith's concept of the ideological code. A managerial imperative pervades this work, even in domestic contexts, and participants made use of workplace genres and conventions.Conclusions. Even in households, the work of keeping track is embedded within organizational contexts. Managerialism is produced and reproduced as an ideological code that shapes participants' information creation and their talk about it.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".