The Potential Bias in Producer Service Employment Estimates: The Case of the Canadian Space Economy
Bibliographic record
Abstract
Footloose information technology producer services are increasingly viewed as the new engines of regional growth and employment creation. The claim is often that appropriate government policy can engineer comparative advantage in that direction by means of judicious subsidisation. The evaluation of such claims relies on accurate statistical data. We argue that attempts to distinguish in survey data or official statistics the subset of services commonly (but unofficially) known as producer services from consumer and government services may result in a potential 'bias' with implications for public policy. An input-output model is developed for estimating this bias by decomposing service industry data into its producer and consumer services content. This method can give a truer estimate of the actual employment in producer services at various spatial levels. We find a systematic bias in official data and a non-systematic bias in survey data. The model is applicable to countries and regions for which input-output data are available.
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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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 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".