Bibliographic record
Abstract
This study observes the Sergian ludling, focussing on its learnability.This study completes a phonological analysis of the language, observing and applying the rule created to explain the rule's role in the ludling's formation.Participants were split into two groups where one had data useful to specific patterns of sesquisyllables withheld, to observe whether the rule they create with the minimal data exposure can extend to data beyond basic epenthesis.The key questions this paper aims to answer are how much data is needed for an individual to be considered exposed to a crucial rule of the language and to what extent do they acquire it.This paper seeks to answer these questions by analyzing participants' audio-recorded responses.Results showed the more exposure one receives, the more likely they are to acquire and correctly apply the rules; having data useful to specific patterns increased chances of participants obtaining higher scores.Professor Lev Blumenfeld, who played a significant role in the outcome of this thesis.He guided me through the writing and research processes, and provided continuous support, expertise, and advice, all of which helped to shape the overall outcome of this paper.All his efforts are very much appreciated, and I am beyond grateful to have had the opportunity to have him as my supervisor.I would also like to personally thank Professor Karen Jesney who served on the advisory committee.Her feedback during the writing stage was one which helped enhance my work.Her continuous advice, knowledge, and direction throughout this process will not be forgotten.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".