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Record W1500455928 · doi:10.18438/b87c8r

Academic Historians in Canada Report Both Positive and Negative Attitudes Towards E-books for Teaching and Research

2013· article· en· W1500455928 on OpenAlexvenueaboutno aff
Heather Coates

Bibliographic record

VenueEvidence Based Library and Information Practice · 2013
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsGrounded theoryPerceptionPsychologyCoding (social sciences)ConfusionQualitative researchContent analysisSocial psychologyMedical educationMedicineSociologySocial science

Abstract

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Objective – To understand academic historians’ attitudes towards, and perceptions of, e-books for use in teaching and research. Design – Qualitative analysis of semi-structured interviews using a grounded theory approach. Subjects – Ten faculty members in departments of history at academic institutions in Southwestern Ontario participated. Methods – Participants were recruited using flyers and email distribution lists. The authors conducted semi-structured interviews lasting 30-60 minutes, between October 2010 and December 2011. After 10 interviews, the authors determined saturation had been reached and ceased recruitment. Interviews were recorded and transcribed for coding. Analysis was conducted using grounded theory procedures incorporating Roger’s Innovation decision model. Main Results – The authors elicited participants' perceptions of e-books without providing a common definition for the concept. Consistent with previous studies, participants were confused about what constituted an e-book, particularly the distinction between e-books and electronic journals and databases. Several comments included illustrate this confusion, indicating the responses collected may represent perceptions of e-resources more generally, rather than e-books in particular. The authors mention that at least one participant who initially responded that they had not used e-books later changed their response as the interview progressed. Unfortunately, the exact number of participants who did so is not reported. Participants reported both negative and positive attitudes towards e-books. Attitudes varied depending on the characteristic discussed. The characteristics identified focused primarily on the delivery mechanism, rather than the content, of e-books. The authors identified four factors each as contributing to positive and negative attitudes. Factors associated with a negative attitude included availability, serendipity, cost, and tradition. These factors stemmed from concerns about changing student research behaviours resulting from the differences between e-books and print books. Factors associated with a positive attitude included convenience, teaching innovations, research practices, and cost benefits. These factors largely reflected benefits to students, such as the ability to access e-books easily (convenience), increased access in general, and the perceived relatively low cost of student e-books. The factor directly benefitting respondents was improved speed and accuracy in their work, enabled by particular technological features. While participants were eager to use e-books in the classroom, there were concerns about implications for research practices. Participants worried that the benefits of browsing and serendipitous discovery would be lost as students chose materials based on convenience rather than other factors, such as quality. Finally, the perceived lack of digitized historical documents available for use as primary sources was also of concern. Conclusions – The authors state that confusion regarding the nature of e-books slows adoption. While participants were exploring ways to incorporate e-books into their norms, values, and research practices, they are unlikely to rely solely on e-books as primary sources. This stems from two perceptions. First, current e-book formats and platforms do not authentically represent all the characteristics of print books. Second, there are insufficient primary sources available as e-books. The validity of these perceptions is not addressed in this article.

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.010
Science and technology studies0.0120.007
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.027
GPT teacher head0.290
Teacher spread0.263 · 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 designObservational
DomainEvaluation
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

Citations1
Published2013
Admission routes2
Has abstractyes

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