Bibliographic record
Abstract
Politicians across Canada have come to understand that our economy improves when we develop so-called “value added” industries and jobs. They suppose that turning raw materials into finished goods creates more value added than simply extracting and exporting raw materials directly. That understanding is entirely and often dangerously wrong. Indeed, the meaning of the phrase “value added” has been so widely misunderstood and distorted that we would all be better off if it were struck from the political rhetoric and public debate entirely. The reality is that almost everything Canadians are being told about which activities add value, and which ones do not, is utterly backwards. Manufacturing has come to be seen as the ultimate source of value added, as though the physical manipulation of matter was somehow responsible. For example, the leader of the federal Opposition, the NDP’s Thomas Mulcair, has insisted that “exporting unrefined heavy oil creates no valueadded jobs” and likens exporting raw logs to “a practice typical of undeveloped nations.” It is not just the NDP, similar statements are made across the political spectrum. Fortunately for us, such views can be put to the test. We will see that they are not just inaccurate, they are the very opposite of the reality: Canada’s raw resource extraction industries actually provide the highest valueadded, often by a significant margin. Oil and gas extraction, for example, creates $1.36 million in value per job per year, 15 times higher than the national average for all sectors and more than triple the value added per job per year in the petroleum products refining sector. Absent such data, is there a better way to think about value added that would provide a clear and intuitive defense to misleading statements? Thankfully, there is: industries that generate the most income are industries with high value added. To say a sector like oil and gas extraction creates no value-added jobs is to say it creates no income, which is plainly false. If replacing “income” for “value added” leads a claim to not make sense, then it is likely false and the politician or commentator should be dismissed. Disturbingly, this mixed-up thinking matters a lot for the health of Canada’s economy. Public policy often favours supposedly high value-added industries at the expense of others through subsidies or other supports. Instead of creating value, when governments favour one sector over another they invariably hurt the economy by distorting the allocation of labour and capital, which lowers Canada’s overall GDP. This is true for any subsidy made on the basis of “value added” – subsidizing resource extraction would also be economically damaging. There may be other reasons to provide industry supports – but value added is never one of them. Canada’s economy, and everyone in it, would be better off if politicians and public commentators put the phrase “value added” to rest.
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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.030 | 0.061 |
| Scholarly communication | 0.036 | 0.023 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.027 | 0.027 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".