An Intelligent Class: The Development Of A Novel Context Capturing Method For The Functional Auto Classification Of Records
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.
Bibliographic record
Abstract
The need to accurately classify records is a core problem in many domains. Historically, the classification of records was done manually, with those records "read" as they were received and categorized. Unfortunately, due to a significant growth in the volume of records, the need for robust auto-classification methods that can effectively "read" and classify records, is high. Today, significant challenges remain in the literature and practice relating to the development of effective, auto-classification processes. This is because the functional classification process is a challenge for both humans and machines, with little research on the steps needed to effectively functionally classify a record. In order to move research forward, this paper will address both challenges. Firstly, this paper, will seek to evaluate the efficacy of manual classifiers on a classification task, using knowledge from this process to articulate a process for functional classification that utilizes a record’s archival diplomatic context. Secondly, this paper will compare the efficacy of manual versus auto-classification using a record set with over 500,000 records, using a novel auto-classification approach that leverages a record’s archival diplomatic context, and not just its content, to improve classification accuracy. As this paper will discuss, there is significant variance between records managers during the manual classification process, with statistically significant differences in their ability to accurately classify both administrative and operational records. Moreover, this paper will demonstrate that an auto-classifier, when trained using key elements of archival diplomatic context, can statistically outperform a group of expert manual classifiers on a classification task.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it