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Record W4386312909 · doi:10.1515/9780888647351-002

Introduction: Points of Departure

2013· book-chapter· en· W4386312909 on OpenAlexaboutno aff
Paul Martin

Bibliographic record

VenueUniversity of Alberta Press eBooks · 2013
Typebook-chapter
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This book is The ProdUcT of nearly two decades of reading and research, taking and teaching courses on the literatures of Canada, and a coast-to-coast journey to visit twenty-eight university literature departments and interview nearly one hundred professors. 1 Much of the formal research for this book began with that exploratory trip in 1997-98 and became a core part of my 2001 PhD dissertation.That text, however, was only an intermediary point to this revised and expanded volume.On its own, the dissertation provided an important snapshot of what I found on my trip and my observations of how the shapes and functions of courses on our literatures are inextricably linked with our literary histories and institutions.The data I gathered in 1997-98 is a valuable historical record in itself, for no one else has ever made an effort to assemble such information in so complete a way.As I demonstrate in Chapters Three and Four of this book, the course data and interviews from that year reveal a tremendous amount of information about what we teach, how we teach, and why we teach the courses that we do.Looking at that snapshot of our field in isolation, however, can only tell us so much, which is why I have not published the results of that initial research until now.Incorporating, as I do in Chapter Five, corresponding data from a full decade later enriches our understanding of that first data set significantly.It is only when we have an understanding of Chapter Four, many professors during that era struggled with the challenge of fitting in newer and more diverse works within the course and department structures that had been in place for some time.The challenge of removing "classic" works that had been on the syllabus for years in order to make room for newer and in some cases "unproven" works was a dilemma that professors raised with me again and again.the challenge for anyone studying the literary institution in English Canada is that there has been relatively little work done on the topic.Moreover, much of what has been written, save the work of a few scholars, including Frank

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.013
GPT teacher head0.168
Teacher spread0.154 · 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
GenreOther

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

Citations2
Published2013
Admission routes1
Has abstractyes

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