Bibliographic record
Abstract
This book covers a lot of ground.The main purpose of the book is to describe and validate a specific schema for solving arithmetic word problems.A number of sophisticated and clever research methodologies are used to provide supporting evidence.In addition to presenting these methodologies and the results of a number of experiments, Marshall describes the implementation of a series of computer-based models designed to replicate the results of these experiments.In the process of providing theoretical support for these ideas, Marshall addresses a number of important related issues in considerable depth.These include discussions of the history and philosophy of schemas and implications of this work for curriculum planning, assessment, and computer-based instructional design.Although some parts of this book will be of interest to philosophers and educators, this is mainly a book for cognitive psychologists.The book starts with a broad historical perspective on what philosophers, psychologists, and others have considered schemas to be.This is followed by an update on current views on schemas in general and, finally, a very specific discussion of the schema Marshall proposes as appropriate for arithmetic story problem solving.The level of detail presented is at the appropriate level to remind readers already familiar with this discipline of the significant participants in this field and their points of view.Readers with less • background will, I'm sure, be sufficiently tantalised to follow up on many of the ideas touched on here.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".