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Record W1570539468 · doi:10.1177/160940690600500101

The Sideshadow Interview: Illuminating Process

2006· article· en· W1570539468 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsReading (process)Interpretation (philosophy)Process (computing)InterviewPsychologyQualitative researchEpistemologyComputer scienceLinguisticsSociologySocial sciencePhilosophy

Abstract

fetched live from OpenAlex

Drawing on the conception of the literary sideshadow, the author describes the development of a sideshadowing interview used to investigate the decision-making processes of writers in a research group. To prepare for the interview, the researcher reads and notates the text that she will discuss with the participant using a process of “close reading.” Sideshadowing interviews ask not only the “why” but also the “why not” and the “what if questions, following a process of both prepared questions and conversational discovery. In the interpretation of a sideshadow interview, the researcher describes how this approach characterizes the complexity of a process. Furthermore, the researcher's biases and influences became readily apparent through this analysis. The author suggests that her conception of the sideshadowing interview is a research technique that might offer useful data to qualitative researchers interested in exploring the nature of processes such as writing, reading, or teaching.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.823
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.358
GPT teacher head0.572
Teacher spread0.213 · 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