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Record W2085398753 · doi:10.1145/782941.782981

E-notes

2003· article· en· W2085398753 on OpenAlex
Michael A. Wirth

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

VenueACM SIGCSE Bulletin · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClass (philosophy)Task (project management)Mathematics educationComplement (music)Note-takingComputer sciencePsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Writing comprehensive, focused notes during a lecture can be a challenging task for students. They often spend most of the time actually writing notes with little time to reflect on the material presented in the lecture, or indeed to ask questions. Electronic lecture notes are a clear and concise electronic form of lecture notes provided openly to students. I use e-notes to complement the material presented in my lectures and experience has shown that they offer a distinct learning advantage over traditional forms of note taking. As students do not have to spend time writing copious notes, they are generally more attentive during class, being able to concentrate on understanding the material. This allows them to become actively engaged in the lecture, participating in class discussions and asking pertinent questions.

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)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.042
GPT teacher head0.386
Teacher spread0.344 · 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