Exploring Undergraduate Admissions through the Development of Shadowing Programs:
Bibliographic record
Abstract
abstract: The thesis titled "Exploring Undergraduate Admissions through the Development of Shadowing Programs" is an organizational study and analysis of a shadowing program developed by Krista Moller, Ryan Johnson, and Kean Thomas. It resulted in the creation of a 25+ person student organization in the W.P. Carey School of Business called "Explore". The organization received backing and support from the admissions department in W.P. Carey, notably Dean of Admissions, Timothy Desch. The organization's members (titled "ambassadors") host a high school student interested in the business school for a day of class. High school students are matched with an ambassador based on majors they might be interested in, and ideally the result of the day of shadowing is the high school student having a better understanding of the opportunities available at W.P. Carey. The organization began in the fall of 2013, and was intended to be used as a thesis project from its inception. As a result, the founder's experiences were carefully documented and this allowed for a detailed analysis to take place. The analysis delves into the difficulties faced by the organization's members and executive board as a result of internal and external influences. The successes and experiences they were fortunate enough to have are also detailed, and plans for the organization's future are included as well. In addition, the Explore program is analyzed in comparison to other programs around the country and even in Canada, with the goal being to see where we could potentially strengthen our program. The founders of the Explore program (and authors of this thesis) hope other students might learn from it so that more programs such as Explore can be created, benefiting the local community and ASU itself.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".