Journal of Economic Perspectives—Volume 18, Number 4—Fall 2004—Pages 93–114 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) called “long-distance, ” purchase of services abroad, principally, but not necessarily, via electronic mediums such as the telephone, fax and the Internet. Outsourcing can happen both though transactions by firms, like phone call centers staffed in Bangalore to serve customers in New York and x-rays transmitted digitally from Boston to be read in Bombay, or with direct consumption purchases by individuals, like when someone hires an offshore firm to provide plans for redesigning or redecorating a living room. Thus, in February 2004, the members of President Bush’s Council of Economic Advisers stated the following: “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 the economy in the long run. We’re very used to goods being produced abroad and being shipped here on ships or planes. What we are not used to is services being produced abroad and being sent here over the
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".