Bibliographic record
Abstract
Performance on word processing tasks is known to be influenced by the frequency with which words occur in a language. Large and robust effects of word frequency occur across languages and the processes thought to be sensitive to word frequency are considered fundamentally important characteristics of the mental lexicon. To our knowledge, word frequency data is non-existent for Urdu. This important language has characteristics that make it appealing to psycholinguists. Unfortunately, most of the Urdu published electronically is in the form of image files rather than text and therefore, has been largely inaccessible by programs designed to generate word counts. Consequently, unlike other important orthographies (e.g., English) orthographic word frequencies in Urdu are not readily available. We describe here a database that addresses this methodological gap. We have constructed a word frequency database for written Urdu and describe that development. We also describe data from simple tests of the effects of Urdu word frequency to demonstrate that our measure results in effects considered to be the hallmark of frequency effects. The frequency counts from this database will help psycholinguists and cognitive psychologists conduct and control future studies on the mental lexicon using Urdu. This database can be downloaded from http://web2.uwindsor.ca/psychology/urdufrequency/
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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".