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Record W2554934898

Information creation and the ideological code of “keeping track”

2014· article· en· W2554934898 on OpenAlexfundaboutno aff
Pamela J. McKenzie, Elisabeth Davies, Sherilyn Williams

Bibliographic record

VenueScholarship@Western (Western University) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaRobert Gordon University
KeywordsIdeologyTrack (disk drive)Code (set theory)Internet privacyComputer scienceComputer securitySociologyPolitical scienceProgramming languageLawOperating systemPolitics
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.349
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes2
Has abstractyes

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