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Record W2903691445 · doi:10.1111/medu.13778

Tensions that define the State of our Science in 2019

2018· editorial· en· W2903691445 on OpenAlexaff
Kevin W. Eva

Bibliographic record

VenueMedical Education · 2018
Typeeditorial
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCynicismMistakeNothingSkepticismEnthusiasmCriticismConversationEpistemologySociologyPolitical sciencePublic relationsPsychologyLawSocial psychologyPoliticsPhilosophy

Abstract

fetched live from OpenAlex

“That's just the latest fad” - someone at every education conference ever “There has been nothing new for over 30 years” - an unfortunately curmudgeonly senior statesman at my first education conference As I imagine ways to introduce the theme of this year's State of the Science issue, I find myself wanting to cover ground that has already been trod in this space.1 I risk repetitiveness, however, because I continue to be reminded of the duelling banjos that seem present in every conversation about education research and innovation: contagious enthusiasm and dampening criticism. Used carefully, the caution represented by the quotes above indicates an appropriate and rational scepticism that should form the foundation for any truly scholarly enterprise. Used reflexively, they represent a problematic cynicism that prevents generative discussion from taking place regarding the contextually bound strengths and weaknesses of educational strategy. Why might such cynicism arise? For one thing, the longer one has been in the field the more ‘solutions’ one will have seen come and go and the more frustrated one might become by the field continuing to grapple with the same issues. Problems that have been recognised for decades continue to exist, allowing negativity to seep in from many directions and making it easy to mistake long-term and ongoing struggle for lack of progress.2 Further, it is easy to lose sight of the fact that not everyone shares the same perspective or priorities. As a result, ‘solutions’ can always be criticised for not addressing every aspect of multifaceted problems by not prioritising ‘the right’ aspect relative to one's own perspective (and can easily harken back to other things that failed for the same reasons). I would argue that the very fact we continue to struggle with long-term problems is by itself an indication that the field is evolving, given that no field or discipline dries up as quickly as those that stop generating new and useful ideas.1 Counter-intuitively, we continue to make progress by sensibly grappling with problems that may be unsolvable in any universal sense. With countless perspectives being brought to bear on any particular issue, there is always an opportunity to develop new understanding by identifying new holes that open whenever we attempt to patch particular leaks. That is not a particularly comfortable position for those who desire ‘the’ answer. However, as the field has matured and come to recognise the value that a variety of perspectives has to offer, many have begun to recognise complexity and turned a lens inward to shine light on the underlying reasons why many of the problems with which we grapple do seem unsolvable. To use an increasingly popular enactment of that notion, such problems are ‘wicked’ in that they resist resolution because there are many complex interdependencies that cause each effort to solve one aspect of the problem to create or reveal other problems.3, 4 This can create a series of tensions as different priorities yield different ‘solutions’. It is the existence (and perhaps inevitability) of the tensions that wicked problems create that this year's State of the Science issue was constructed to highlight. When the deputy editors brainstormed topics that warranted a deliberate examination of where the field stands in 2019, it became apparent that many suggestions, either alone or in combination, highlighted tensions that seem omnipresent in our field and are unlikely to ever simply go away. By ‘tensions’ I mean pressures that contradict each other in manners that can make important issues unresolvable or at least lead inevitably to conflicting yet equally valuable opinions/priorities. That is, they are competing perspectives that aren't necessarily right or wrong, but need always to be balanced in one way or another.5 As Moroz points out in his commentary, there are a variety of ways in which we respond to tension, some of which lead us around in circles, and some of which hold the potential to offer new insights by offering a change of perspective or re-framing of the problem being considered.6 The ‘wickedity’ of our problems, after all, need not disempower efforts to engage in quality improvement. Rather, wickedity reinforces the requirement that we shift focus away from trying to prove any one solution correct towards scholarship that seeks to better understand the problem.7 That enables local efforts to take into account broader insights regarding when, where and how different interventions might be applied most effectively. This is not an argument for prioritising theory over practice, but is instead the logic behind continuing the movement towards collectively addressing how we might better anticipate and reflect upon the unintended consequences that will arise with any practical intervention. Similarly, the purpose of placing tensions at the centre of our 2019 State of the Science discussion is not to resolve the tension by weighing the value of competing arguments. Rather, it is to offer explicit reflection on tensions that might be used more effectively if more clearly elaborated. To that end, the editors curated a series of articles looking at tensions in our field that address educational programming (e.g. the tension between standardisation and contextualisation8), learning practices (e.g. the tension in presenting oneself as both credible and vulnerable9) and assessment issues (e.g. the tension between focusing on what we know we can measure and what we think we should measure10), as well as medical education research itself.11 Our hope in doing so is that the efforts made by each set of authors (and their peer reviewers) help to strengthen the utility inherent in each of the factors responsible for creating tension and move us some degree beyond committing the error Einstein warned against of attempting to ‘solve our problems with the same thinking we used when we created them’.

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)0.005
metaresearch head score (Gemma)0.067
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.063
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.014
GPT teacher head0.381
Teacher spread0.367 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations6
Published2018
Admission routes1
Has abstractyes

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