Bibliographic record
Abstract
National income accounts view most business expenditures on intangible goods as acquisitions of intermediate inputs that get entirely used up in the production of final output. After arguing against this convention, I construct a data set to document firms ’ expenditures on an identifiable list of intangible items for which there is now wide agreement among national accountants. I then examine the implications of treat-ing intangible spending as an acquisition of final (investment) goods on GDP growth for Canada. I find that investment in intangible capital during the years 1998 to 2004 is as large as the investment in physical capital. This result is in line with similar findings for the US, the UK and Japan. Furthermore, the growth in GDP and labor productivity may be underestimated by as much as 0.1 % during this same period. In light of current debates at various statistical agencies regarding capitalizing intangi-bles, this study confirms the need to indeed consider such expenditures as investments
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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 | 0.000 | 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) | 0.015 | 0.006 |
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; both teacher heads agree on what is shown here.
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".