(To be published in revised form in: The Journal of Economic Perspectives) The Muddles over Outsourcing
Bibliographic record
Abstract
In the early 1980s, “outsourcing ” typically referred to the situation when firms expanded their purchases of manufactured physical inputs, like car companies that purchased window cranks and seat fabrics from outside the firm rather than making them inside. But in 2004, outsourcing took on a different meaning. It referred now to a specific segment of the growing international trade in services. This segment consists of arm’s-length [or what Bhagwati (1984) has called “long-distance”] purchase of services abroad, principally, but not necessarily, via the electronic mediums such as the telephone, fax and Internet and includes, for example, phone call centers staffed in Bangalore to serve customers in New York and x-rays transmitted digitally from Boston to be read in Bombay.1 Thus, in February 2004, the members of President Bush’s Council of Economic Advisers stated: “Outsourcing of professional services is a prominent example of a new type of trade ” (Mankiw, Forbes, and Rosen, 2004). The chair of the CEA, Gregory Mankiw, made a similar point in a press interview (Andrews, 2004): ''I think outsourcing is a growing phenomenon, but it's something that we should realize is probably a plus for
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.294 | 0.084 |
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".