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Record W2001155351 · doi:10.1055/s-0032-1311674

A Grounded Theory Primer for Audiology

2012· article· en· W2001155351 on OpenAlexaff
Stella Ng

Bibliographic record

VenueSeminars in Hearing · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsGrounded theoryQualitative researchConstructivist grounded theoryVariety (cybernetics)EpistemologyField (mathematics)SociologyComputer scienceSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Grounded theory is a widely used qualitative research methodology and has been used in a variety of disciplines including anthropology, medicine, nursing, and sociology. Although qualitative research methodologies, including grounded theory, have existed for some time, there is lack of attention to the rigorous use of qualitative methodology in the field of audiology. Today, three major schools of grounded theory are present in the literature. It is important for clinicians and researchers new to grounded theory research to be familiar with the different schools of grounded theory that can be implemented in research including the history and philosophy of each. Such an understanding will enable researchers and clinicians to critically appraise applications of grounded theory methodology and to understand its potential, perhaps for inclusion in their own research programs. This article will provide a brief history of grounded theory, define the three major schools of grounded theory and the philosophical and theoretical underpinnings of each, as well as discuss the use of grounded theory in audiology research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.944
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.078
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.008
Science and technology studies0.0030.009
Scholarly communication0.0090.007
Open science0.0070.008
Research integrity0.0070.019
Insufficient payload (model declined to judge)0.0210.008

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 head0.229
GPT teacher head0.545
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations9
Published2012
Admission routes1
Has abstractyes

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