Kollmeyer_AmJournSocio_2009_EJpm - Méndez-Chacón - 288ok
Bibliographic record
Abstract
This is a data-analytic replication attempt on the following claims from Kollmeyer et al. (2009): ● Trace number: 1 / Claim ID: m5y94l Claim 4 (Result statement): Table 1 shows the results from three regression models, each capturing some portion of the direct effects causing deindustrialization…Model 1 isolates the two domestic factors believed to be associated with deindustrialization: (1) the tendency for growing affluence in wealthy countries to spur demand for services more than manufactured goods…As anticipated, the coefficients for these variables are statistically significant, and they exhibit the expected signs. The results for national affluence’s effect on manufacturing employment deserve particular attention. Here the results indicate that the coefficient for national affluence is positive, the coefficient for national affluence squared is negative, and the coefficient for national affluence cubed is again positive. [TABLE 1, Model 1, National affluence: 3.171, SE = .267, P < .001; (National affluence)^2: -.154, SE = .012, P < .001; (National affluence)^3: .002, SE = .000, P < .001] ● Trace number: 2 / Claim ID: my1dxd Claim 4 (Result statement): Table 1 shows the results from three regression models, each capturing some portion of the direct effects causing deindustrialization…Model 1 isolates the two domestic factors believed to be associated with deindustrialization:…(2) the propensity for productivity gains in the manufacturing sector to exceed those made by other sectors of the economy. As anticipated, the coefficients for these variables are statistically significant, and they exhibit the expected signs…The results from model 1 reveal a nonlinear relationship between unbalanced productivity growth and relative manufacturing employment as well. Here the coefficient for unbalanced productivity growth is negative, but the coefficient for unbalanced productivity growth squared is positive. [TABLE 1, Model 1, Unbalanced productivity growth: -10.192, SE = .875, P < .001; (Unbalanced productivity growth)^2: 2.516, SE = .266] ● Trace number: 3 / Claim ID: b29xjd Claim 4 (Result statement): Model 2, shown in table 1, isolates the global economic factors purportedly associated with deindustrialization. Results from this model support the view that expanding trade links between the North and the South of the global economy contribute to deindustrialization. The specifics of this relationship become clearer when looking at the individual coefficients for the variables constituting North-South trade. Expressed in absolute values, the coefficient for imports from the South (b = -0.828) is more than three times larger than the coefficient for exports to the South (b = 0.192). The imbalance between these two coefficients implies that North-South trade, rather than generating counterbalancing effects on domestic manufacturing employment, actually displaces more than four times as many jobs as it creates. [coefficient for imports from the South…is more than three times larger than the coefficient for exports to the South; TABLE 1, Model 2, Imports from the South: -.828, SE = .103, P < .001; Exports to the South: .192, SE = .079, P < .001] ● Trace number: 4 / Claim ID: m8oy76 Claim 4 (Result statement): Model 3, shown in tables 1 and 2, offers a more comprehensive analysis by simultaneously testing each variable…Under this combined model, the size and statistical significance of most coefficients exhibit little change from the previous two models. [TABLE 1, Model 3, National affluence: 2.555, SE = .275, P < .001; (National affluence)^2: -.124, SE = .012, P < .001; (National affluence)^3: .002, SE = .000, P < .01] ● Trace number: 5 / Claim ID: bonjlw Claim 4 (Result statement): Model 3, shown in tables 1 and 2, offers a more comprehensive analysis by simultaneously testing each variable, except FDI, which was excluded due to its statistical insignificance. Under this combined model, the size and statistical significance of most coefficients exhibit little change from the previous two models. [TABLE 1, Model 3, Unbalanced productivity growth: -10.070, SE = .882, P < .001; (Unbalanced productivity growth)^2: 2.302, SE = .267, P < .001] Deviations from the original study: 1. The original study includes 18 Organization for Economic Cooperation and Development (OECD) countries from 1970 to 2003. The data used in the replication contains 16 OECD countries from 2004 to 2018 (including 10 countries from the original study). Countries in the original study: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States. Countries in the replication: Australia, Austria, Czech Republic, Denmark, Finland, Germany, Hungary, Italy, Japan, Korea, New Zealand, Norway, Poland, Portugal, Slovak Republic, and the United Kingdom. All the new countries belong to the “North” category (https://en.wikipedia.org/wiki/Global_North_and_Global_South). 2. The data sources are the same as the original study, except for the trade flows, that are obtained from UN Comtrade, instead of the OECD International Trade by Commodities Database. However, because both dataset measures trade flows across countries, the amount should be similar and therefore the impact on the replication results should be minimal.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.549 | 0.261 |
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; the direct Gemma label and the distilled Codex classifier 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".