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Record W4238378750 · doi:10.1364/etop.2007.etb2

University of Toronto Institute for Optical Sciences Collaborative Program in Optics

2007· article· en· W4238378750 on OpenAlexaffabout
Emanuel Istrate, Amr S. Helmy, John E. Sipe, M. Cynthia Goh, R. J. Dwayne Miller

Bibliographic record

VenueEducation and Training in Optics and Photonics · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumSet (abstract data type)Graduate studentsComputer scienceLibrary scienceMedical educationEngineering physicsEngineering ethicsEngineeringSociologyPedagogyMedicine

Abstract

fetched live from OpenAlex

We describe the activities of the Institute for Optical Sciences (IOS) at the University of Toronto towards the establishment of a Master’s Program in Optics. The IOS was formed as a collaboration between faculty members interested in optics from the four departments of Physics, Chemistry, Electrical and Computer Engineering and Materials Science and Engineering. One of its goals is to serve as unifying entity for graduate and undergraduate programs in optical sciences. The details of the proposed graduate program will be discussed. It will be set up in the form of a collaborative university program, where students must satisfy the requirements of one of the four home departments, as well as a set of IOS-specific requirements of the program. IOS-specific activities include attending the Distinguished Visiting Scientist Series, participation in a best-research-practice mini-course, where essential research skills are discussed, as well as participation in an annual internal conference. The benefits of this interdisciplinary program, for students, faculty and relevant industries are discussed. The students will benefit from a wider exposure and a more coherent curriculum. The IOS will also serve as local community within the campus to which students could belong and network. Faculty, on the other hand, will benefit from a reduced teaching load, as redundancies among the departments will be removed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.048
GPT teacher head0.419
Teacher spread0.371 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2007
Admission routes2
Has abstractyes

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