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Record W2049046549 · doi:10.1177/1075547006298251

Poster Presentations as a Genre in Knowledge Communication: A Case Study of Forms, Norms, and Values

2007· article· en· W2049046549 on OpenAlexaff
Anu MacIntosh‐Murray

Bibliographic record

VenueScience Communication · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDisk formattingPresentation (obstetrics)Competence (human resources)PrestigeSocial mediaCurrencyAffect (linguistics)Computer sciencePsychologyMultimediaWorld Wide WebSocial psychologyLinguistics

Abstract

fetched live from OpenAlex

Learning to communicate research well through posters involves far more than formatting issues such as font size. The conventions of poster presentations as social practices are part of academic apprenticeship in many health disciplines. This case study examines doctoral students’ poster presentations as a research-process genre. The article maps genre knowledge required of novice researchers: poster form, creation processes, presentation practices, and underlying values. Complexity arises from the multiple roles that posters must fulfill, combined with formatting restrictions, the nature of audience interaction, and prestige issues. Posters are often considered as second class compared to oral presentations, perhaps unfairly. The reuse of posters raises questions about publication as academic currency and appropriate knowledge-exchange practices. Poster presentations are evolving with digital media, which may affect competence development in this multimodal form of research communication. Future research should consider how posters’ technology-influenced evolution affects interaction, communicative purposes, and the texts themselves.

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)0.023
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0170.012
Scholarly communication0.0100.008
Open science0.0020.007
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.074
GPT teacher head0.369
Teacher spread0.295 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations56
Published2007
Admission routes1
Has abstractyes

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