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Record W3194173068 · doi:10.1080/13645579.2021.1964858

Adaptive methodology. Topic, theory, method and data in ongoing conversation

2021· article· en· W3194173068 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 Social Research Methodology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMemorial University of NewfoundlandUniversity of Alberta
Fundersnot available
KeywordsComputer scienceConversationPremiseFraming (construction)StructuringProcess (computing)Adaptation (eye)LimitingManagement scienceResearch methodologyEpistemologyData scienceSociologyPsychology

Abstract

fetched live from OpenAlex

This paper explores the concept of adaptive research design, in which topic, theoretical framing, method, and data are in principle open to adaptation during the research process. The main premise is that adaptations in one element of the research process can trigger changes in other elements. Both positive and negative reasons for adaptivity are discussed along with various valid reasons for limiting adaptivity in particular cases. Grasping the different couplings between concepts, theories and methods is useful to discern the possibilities and limits of adaptive methodology in situ. To deepen the understanding of the adaptive capacity of methodology, we broaden the discussion to look at the embedding of methodology in academia and its disciplines. In our perspective, methods appear as devices structuring thinking and observation and are well used and placed if they enhance and enable the continuation of observation and reflection and if they allow the researcher to remain open for alternative observations and interpretations.

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)0.189
metaresearch head score (Gemma)0.179
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.386
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1890.179
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.949
GPT teacher head0.789
Teacher spread0.161 · 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