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Record W4408151498 · doi:10.3991/ijac.v18i1.52273

Beyond Learner Reaction: Measuring the Impact of Leadership Development at The Ivey Academy

2025· article· en· W4408151498 on OpenAlexaff
Rosa Cendros Araujo

Bibliographic record

VenueInternational Journal of Advanced Corporate Learning (iJAC) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsIvey Foundation
Fundersnot available
KeywordsPsychologyComputer scienceKnowledge management

Abstract

fetched live from OpenAlex

This paper provides an in-depth exploration of The Ivey Academy’s transition from traditional satisfaction-based evaluations to a more comprehensive impact evaluation approach in leadership development. Recognizing the limitations of relying solely on participant satisfaction, The Ivey Academy adopted a modified framework inspired by the Kirkpatrick Model, which evaluates satisfaction, learning, application, and longterm impact. This framework utilizes a range of data collection tools, including surveys, interviews, and action plans. The paper details the implementation process, from securing stakeholder engagement to designing effective surveys and overcoming the challenges of resistance and operational limitations. A key focus of the paper is on the impact survey results from the first term of open enrollment programs, which demonstrate significant improvements in workplace behavior and leadership strategies among participants. Additionally, it highlights the challenges in ensuring data comparability across diverse programs and audiences. Looking ahead, the paper discusses future directions for The Ivey Academy, emphasizing the refinement of the evaluation process, expanding impact measurement, and exploring standardization across various leadership development programs. This approach underscores The Ivey Academy’s commitment to driving realworld change through leadership education, offering valuable insights for other institutions aiming to adopt similar evaluation practices.

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)0.006
metaresearch head score (Gemma)0.004
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 score0.462
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.221
GPT teacher head0.440
Teacher spread0.219 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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