Unpacking the Role of Big Data, Artificial Intelligence, and Predictive Analytics in Education
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
Goodbye to ChatGPT (chat generative pre-trained transformer), hello to AI (artificial intelligence) on the moon! AI is daring to have its finger touching the surface of the moon. The CMCSS (Canadian Mission Control Space Services) through budgetary funding of $3.04 million by the Canadian Space Agency made history when it launched the Rashid Rover on 11 December 2023, with the aim of spending one lunar day in space. The mission will see the Rover capturing and identifying geological features through pictures, and it was motivated by CMCSS’ urge to be the pioneer in showcasing AI’s DL (deep learning) capabilities first in lunar space. DL is a subset of ML (machine learning) and it relies on large and vast volumes of data, based on complex algorithms to train the model (Rane, Kaya, Mallick, & Rane 2024:218).
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 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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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