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Record W2173745299 · doi:10.19030/ajbe.v6i1.7486

Can Business Students Forecast Their Own Grade?

2012· article· en· W2173745299 on OpenAlex

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

VenueAmerican Journal of Business Education (AJBE) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsBusiness educationMathematics educationLikert scalePsychologyBusinessHigher educationEconomics

Abstract

fetched live from OpenAlex

This study examines grade expectations of two groups of business students for their final course mark. We separate students that are on average “better” forecasters on the basis of them not making significant forecast errors during the semester from those students that are poor forecasters of their final grade. We find that the better forecasters are students that have a higher final grade on average than the poor forecasters. The sample evidence indicates that students’ are overconfident, as indicated by their initial grade expectations, irrespective of ability to forecast. But these expectations change during the semester in the downwards direction as students accumulate information on their performance. As expected the poor forecasting students have much more sluggish expectations.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.046
GPT teacher head0.411
Teacher spread0.366 · 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