An Analysis of the Postal Code Conversion File's Use in Research
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
This research paper examines how the Statistics Canada Postal Code Conversion File (PCCF) is being used by researchers. The study used a systematic search strategy to locate publications that incorporated the use of the PCCF into the underlying research process. The retrieved publications were then reviewed, and data was collected for several variables such as year of publication, type of publication, researcher’s discipline or field, and category of PCCF usage. Analysis of the results found that the Data Liberation Initiative program was definitely a factor in increasing the use of the PCCF among academic researchers. It also established that researchers from the health sciences and medical fields were the predominant users of the PCCF. With regards to the category of usage, the study has discovered that most researchers use the PCCF for the following purposes: 1) to aggregate research data to census geographic units; 2) to link research data (individual or aggregated) with the corresponding census data; 3) to determine the rural/urban geographic location of their subjects; 4) to measure distance; and, 5) to map data.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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