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Record W2057484739 · doi:10.1111/medu.12242

‘When I use a word, it means just what I choose it to mean – neither more nor less’

2013· editorial· en· W2057484739 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

VenueMedical Education · 2013
Typeeditorial
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMeaning (existential)Simple (philosophy)Word (group theory)PsychologyLinguisticsEpistemologyPhilosophyPsychotherapist

Abstract

fetched live from OpenAlex

When Humpty-Dumpty took a cameo role in Through the Looking Glass, he probably didn't realise that this line would be among those most quoted from the book.1 Little did this diminutive anthropomorphic egg think that over the next hundred or more years, he would inspire judges, presidents and now medical educationalists to ponder on and dissect the meanings of words and phrases (http://en.wikipedia.org/wiki/Humpty_Dumpty). When we use a word, we may have a specific meaning in mind, but that meaning may differ from that within the reader's mind. Sometimes there is genuine controversy regarding the meaning of a concept; sometimes there are a number of different definitions, and sometimes when we use a word, we may not have a specific meaning in mind (i.e. we might use a word loosely or, worse still, lazily). When we use a word, we should define it – but only rarely do we do so, and it is to fill this gap that the idea of a new series in Medical Education was conceived. There are endless examples in the literature that indicate the need for such a series. Even seemingly simple concepts such as cost or cost-effectiveness in medical education can be misunderstood.2, 3 The idea for the ‘When I say …’ series is that we invite authors to write short, interesting, insightful and engaging articles that offer definitions of concepts within any of a number of relevant issues: a central concept in modern models of education (e.g. problem-based learning); a brief overview of methodological features (e.g. grounded theory), or a concept that has been variably defined in the literature (e.g. fidelity in simulation). Is it all just an exercise in semantics? It might well be and we hope that it will be all the richer for that. In modern parlance, semantics has almost become a term of abuse as academics seem to split hairs over topics that no-one really cares about. But when we say ‘semantics’, we hope to return to the original definition of the term: the study of meaning. Inspiration has come from a range of medical educators who narrate stories and conjure metaphors to go with their theories. For example, validity in assessment can be a difficult concept to understand until you read the explanation put forth by Schuwirth and van der Vleuten: ‘…in the way that a thermometer is a valid instrument to measure temperature and only temperature (and not weight for example), an assessment method is valid only for a certain aspect of competence.’4 Thus, the new series challenges authors to make their articles engaging, rather than simply representing a glossary of terms. Another challenge is to make the complex concise. As we, and many others, have noted, it takes more effort to write a short article than to write a long one.5 We are asking our authors to take their time over a short article in the full knowledge that some concepts will be excluded from the series simply because they cannot be presented with due richness in a brief piece. We want these articles to be as simple as possible without being superficial or misleading. To jumpstart the series, we have commissioned authors to write articles for the next few issues. This month starts with triangulation in medical education research.6 From now on, we plan to open the series to all comers. If you would like to write a piece, then contact the editorial office with a proposal by writing to [email protected]. We would be delighted to hear from you. And if you would use a word differently from one of our authors, then please contribute to the discussion threads at www.mededuc.com.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.002

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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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