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Record W4238327751 · doi:10.1002/emt.20040

Table of Contents

2011· article· en· W4238327751 on OpenAlexaboutno aff

Bibliographic record

VenueEnrollment Management Report · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachCitationPopulationPublishingTable of contentsBest practicePublic relationsComputer scienceMedical educationWorld Wide WebPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

Abstract Cover Story Improve outreach to adult prospects with a CRM system Reach nontraditional prospects with CRM Engage campus in CRM use Talisma CRM offers speed, features Idea File Learn best practices for retaining Hispanic students Use new Facebook page format for a creative impact Consider online interviews to help choose the best students Create opportunities for students with intellectual disabilities Strategies for Success Make best use of campus architecture in enrollment strategy Consider architecture as an enrollment tool Trends Prepare to enroll a changing student population Expect more students to seek online courses Compliance Know FERPA rules regarding registered sex offenders Washington Report Know how credit‐hour definition applies to brick‐and‐mortar, online classes Review reasons 70 groups objected to federal credit‐hour definition Managing Your Office Ensure compliance with accessibility requirements Lawsuits & Rulings DISCIPLINE Student returns to nursing program after publishing placenta photos ACADEMIC AFFAIRS Poor academic performance led to law student's dismissal ADMISSIONS Race‐conscious admissions upheld at state university DISCRIMINATION Court green‐lights noneconomic damages in race discrimination claim Leaders & Innovators SUSAN GOTTHEIL, VICE PROVOST (STUDENTS), UNIVERSITY OF MANITOBA Promote collaboration between student, academic affairs Promote collaboration with these strategies

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.084
GPT teacher head0.335
Teacher spread0.251 · 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
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

Citations0
Published2011
Admission routes1
Has abstractyes

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