Mass schooling, empowerment, and demographc and economic outcomes: a note of dissent. Vienna Yearbook of Population Research|Vienna Yearbook of Population Research 2010 8|
Bibliographic record
Abstract
In this note, I use as a peg a recent publication by Lutz et al. (2009) because it summarises, develops and supports many of the main arguments for the policy focus on mass universal schooling that has become the mantra in development planning during the last quarter century or so.I also use this paper because it is so comprehensive, so clearly written and firm in its general conclusions even as it recognises that there is much more research to be done.But most of all, I use it because I wish I could have plagiarised the title of one of its references-an essay Lutz wrote and published as a 15 year old schoolboy-not only because it is a charming title but because it (the title, I don't have access to the actual essay) seems to say what I too want to say (albeit much more hesitantly than an impetuous schoolboy) in this cautionary note.Lutz's boyhood essay was entitled "Elementary School: A Crime on Children".Before I make that lament the subject of my own essay here, there are some clarifications and caveats to underline what my short paper is not.It is not a rabble rouser's attempt to discredit the huge investments in public schooling toward which the international development community, including the international research community, has been pressurising national governments so as to make universal and compulsory primary schooling a national commitment, contrary to Lutz's (2009) apprehension.Goal 2 of the Millennium Development Goals-all children to complete a full course of primary schooling by 2015-is only one natural outcome of the consensus that such a commitment will lead to a better world economically, socially and politically.I agree that acting on such a commitment will be a welcome first step, but that is all it can be-only a first step towards a much more ambitious agenda of giving everyone a much higher level of education than the four or five years that primary school requires.I say this in spite of the evidence that the old adage-that a little learning is a dangerous thing-seems to be contradicted for most development outcomes.I concede that primary school attainment rates are strong predictors of national income and productivity as well as of improvements in infant and child mortality
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".