2024-02-08 14:48:29 +00:00
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# Meeting Miguel 2024-02-09
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achieved:
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2024-02-09 15:53:50 +00:00
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- [ ] remaining study batch screened
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- added active labour market policy studies
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- starting to wrangle synthesis:
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- better categorization for some and did dual categorizations, e.g. job search assistance or subsidized employment/wages partly into ALMP
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- Moved away again a little from strict framework adherence to make more sense for the synthesis -> workfware programmes
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2024-02-08 14:48:29 +00:00
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- [ ] focus now:
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2024-02-09 15:53:50 +00:00
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- table reviewing main findings
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2024-02-08 14:48:29 +00:00
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- screen the remaining 200 studies
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- improve the writing on the synthesis itself
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- to adhere a little closer to
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- [ ] questions:
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- logistical:
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- create extraction matrices with main/statistical findings
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- are we going to put those in the main Appendix?
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or do we just pass on the CSV extraction as the data?
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- is there a specific length I am going for for the end result?
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- is it fine if for now I leave intro/conclusion more vague to fit in with rest of the document
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- writing:
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- categories overlap sometimes, of course
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- a study on NREGS in India which has minimum wage effects as primary channel
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- a study on vocational training which fuses it with workfare program (ALMP)
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2024-02-09 15:53:50 +00:00
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- I would put the focus on 'per-area' conclusions, not the 'per-study'
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- validity visualizations
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- built a simple scoring rubric of 0-not fulfilled, 1-implicitly fulfilled, 2-explicitly fulfilled
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- internal: (design + method) - (selection bias + measurement bias + confounding bias + performance bias)
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- external: (representativeness) - (generalizability concerns + population invalidity + ecological invalidity)
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- internal:
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- design (observational/qualitative, correlational, causal(quasi-experimental/experimental))
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- confounding variable control (no mention, limits mentioned, limits mentioned and explicitly discussed)
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- data collection (not mentioned, sources mentioned, sources/sample size/observation-length given)
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- statistical control (no robustness checks, data robustness mentioned, explicit robustness checks)
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- biases (no mention, possible biases mentioned, explicitly controlled for/influcence explained)
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- external:
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- generalizability (no mention, implicitly generalizable, explicitly generalizable)
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- representativeness of sample (no mention/sub-national, national, regional/global)
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- ecological validity (no mention, implicit policy implication, explicit policy implication)
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2024-02-08 14:48:29 +00:00
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- [ ] TODO
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- main findings table still remaining
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- write it out
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- shorten?
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