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feat(script): Move detailed search protocol to Appendix

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@ -116,39 +116,17 @@ Study inclusion and exclusion scoping criteria
## Search protocol
The search protocol follows a three-staged process of execution: identification, screening and extraction.
First, in identification, the relevant policy, inequality and world of work related dimensions are combined through Boolean operators to conduct a search through the database repository Web of Science and supplemental searches via Google Scholar to supply potential grey literature.
While the resulting study pools could be screened for in multiple languages, the search queries themselves are passed to the databases in English-language only.
Relevant results are then complemented through the adoption of a 'snowballing' technique,
in which an array of identified adjacent published reviews is analysed for their reference lists to find cross-references of potentially missing literature and in turn add those to the pool of studies.
To identify potential studies and create an initial sample, relevant terms for the clusters of world of work, inequality and policy interventions have been extracted from the existing reviews as well as the ILO definitions.[^existingreviews]
[^existingreviews]: TODO: citation of existing reviews used; ILO definitions if mentioned
Identified terms comprising the world of work can be found in the Appendix tables @appatbl-wow-terms, @appatbl-intervention-terms, and @appatbl-inequality-terms,
with the search query requiring a term from the general column and one other column of each table respectively.
Each cluster is made up of a general signifier (such as “work”, “inequality” or “intervention”) which has to be labelled in a study to form part of the sample,
as well as any additional terms looking into one or multiple specific dimensions or categories of these signifiers (such as “domestic” work, “gender” inequality, “maternity leave” intervention).
For the database query, a single term from the respective general category is required to be included in addition to one term from any of the remaining categories.
Second, in screening, duplicate results are removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on:
language, title, abstract, full text and literature supersession through newer publications.
Properties in these characteristics are used to assess an individual study on its suitability for further review in concert with the inclusion criteria mentioned in @tbl-inclusion-criteria.
To facilitate the screening process, with the help of 'Zotero' reference manager a system of keywords is used to tag individual studies in the sample with their reason for exclusion,
such as 'excluded::language', 'excluded::title', 'excluded::abstract', and 'excluded::superseded'.
This keyword-based system is equally used to further categorize the sample studies that do not fall into exclusion criteria, based on primary country of analysis, world region, as well as income level classification.
To that end, a 'country::', 'region::' and 'income::' are used to disambiguate between the respective characteristics, such as 'region::LAC' for Latin America and the Caribbean, 'region::SSA' for Sub-Saharan Africa; as well as for example 'income::low-middle', 'income::upper-middle' or 'income::high'.
These two delineations follow the ILO categorizations on world regions and the country income classifications based on World Bank income groupings [@ILO2022].
Similarly, if a specific type of inequality, or a specific intervention, represents the focus of a study, these will be reflected in the same keyword system (such as 'inequality::income' or 'inequality::gender').
The complete process of identification and screening is undertaken with the help of the Zotero reference manager.
Last, for extraction, studies are screened for their full-texts, irrelevant studies excluded with 'excluded::full-text' as explained above and relevant studies then ingested into the final sample pool.
The search protocol followed a three-staged process of execution: identification, screening and extraction.
A detailed description of each review step and relevant terms can be found in the Appendix.
<!-- TODO: create cross-reference to relevant appendix section, needs to be numbered? -->
First, in identification, the relevant policy, inequality and world of work related dimensions were combined through Boolean operators to conduct a search through the database repository Web of Science and supplemental searches via Google Scholar to supply potential grey literature.
Second, in screening, duplicate results were removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on:
language, title, abstract, full text and literature superseded through newer publications.
Properties in these characteristics were used to assess an individual study on its suitability for further review in concert with the inclusion criteria mentioned in @tbl-inclusion-criteria.
Should any literature reviews be identified as relevant during this screening process,
they will in turn be crawled for cited sources in a 'snowballing' process.
The sources will be added to the sample to undergo the same screening process explained above,
The sources are then be added to the sample to undergo the same screening process,
ultimately resulting in the process represented in the PRISMA chart in @fig-prisma.
```{mermaid}
@ -638,7 +616,7 @@ a quick rise in less physically-oriented occupations in Brazil,
the introduction of a feminised manufacturing sector Mexico in the 1990s,
and more subsistence-oriented labour markets with diverging skill structures in Thailand and India.
[^rendall-brain-brawn]: They use a framework which they term 'brawn' (physical labour) to 'brain' (less physically demanding labour, such as office work and service economy). The concept sees capital displacing brawn in production for transition economies which they find confirmed in all countries, though to different extents.
[^rendall-brain-brawn]: The study uses a framework termed 'brawn' (physical labour) to 'brain' (less physically demanding labour, such as office work and service economy). The concept sees capital displacing brawn in production for transition economies which they find confirmed in all countries, though to different extents.
As with the study's results for wage gap fluctuations, there are a variety of mediating factors at play in each context, some of which may be unidentified.
@Wang2020 in turn use a simulation to focus on the spatial income inequality effects of terminating subsidies for the agricultural grain sectors in China.
@ -1056,7 +1034,37 @@ Another reason could be the actual implementation of different policy programmes
# Appendices {.appendix .unnumbered}
## Appendix A - Term clusters {.unnumbered}
## Appendix A - Term clusters {#sec-search-protocol .unnumbered}
The search protocol followed a three-staged process of execution: identification, screening and extraction.
First, in identification, the relevant policy, inequality and world of work related dimensions were combined through Boolean operators to conduct a search through the database repository Web of Science and supplemental searches via Google Scholar to supply potential grey literature.
While the resulting study pools could be screened for in multiple languages, the search queries themselves were passed to the databases in English-language only.
Relevant results were then complemented through the adoption of a 'snowballing' technique,
in which an array of identified adjacent published reviews is analysed for their reference lists to find cross-references of potentially missing literature and in turn add those to the pool of studies.
To identify potential studies and create an initial sample, relevant terms for the clusters of world of work, inequality and policy interventions have been extracted from the existing reviews as well as the ILO definitions.[^existingreviews]
[^existingreviews]: TODO: citation of existing reviews used; ILO definitions if mentioned
Identified terms comprising the world of work can be found in the Appendix tables @appatbl-wow-terms, @appatbl-intervention-terms, and @appatbl-inequality-terms,
with the search query requiring a term from the general column and one other column of each table respectively.
Each cluster is made up of a general signifier (such as “work”, “inequality” or “intervention”) which has to be labelled in a study to form part of the sample,
as well as any additional terms looking into one or multiple specific dimensions or categories of these signifiers (such as “domestic” work, “gender” inequality, “maternity leave” intervention).
For the database query, a single term from the respective general category is required to be included in addition to one term from any of the remaining categories.
Second, in screening, duplicate results were removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on:
language, title, abstract, full text and literature superseded through newer publications.
Properties in these characteristics were used to assess an individual study on its suitability for further review in concert with the inclusion criteria mentioned in @tbl-inclusion-criteria.
To facilitate the screening process, with the help of 'Zotero' reference manager a system of keywords is used to tag individual studies in the sample with their reason for exclusion,
such as 'excluded::language', 'excluded::title', 'excluded::abstract', and 'excluded::superseded'.
This keyword-based system is equally used to further categorize the sample studies that do not fall into exclusion criteria, based on primary country of analysis, world region, as well as income level classification.
To that end, a 'country::', 'region::' and 'income::' are used to disambiguate between the respective characteristics, such as 'region::LAC' for Latin America and the Caribbean, 'region::SSA' for Sub-Saharan Africa; as well as for example 'income::low-middle', 'income::upper-middle' or 'income::high'.
These two delineations follow the ILO categorizations on world regions and the country income classifications based on World Bank income groupings [@ILO2022].
Similarly, if a specific type of inequality, or a specific intervention, represents the focus of a study, these will be reflected in the same keyword system (such as 'inequality::income' or 'inequality::gender').
The complete process of identification and screening is undertaken with the help of the Zotero reference manager.
Last, for extraction, studies are screened for their full-texts, irrelevant studies excluded with 'excluded::full-text' as explained above and relevant studies then ingested into the final sample pool.
::: {#appatbl-wow-terms}
@ -1097,12 +1105,12 @@ Inequality term cluster
::: {#appbtbl-validity-external}
| Representativeness | Ranking |
| --- | --- |
| non-representative survey/dataset | 2.0 |
| subnationally representative survey/dataset | 3.0 |
| nationally representative survey/dataset | 4.0 |
| census-based dataset | 5.0 |
| Representativeness | Ranking |
| --- | --- |
| non-representative survey/dataset | 2.0 |
| subnationally representative survey/dataset | 3.0 |
| nationally representative survey/dataset | 4.0 |
| census-based dataset | 5.0 |
External validity ranking. Adapted from @Maitrot2017.
@ -1111,7 +1119,7 @@ External validity ranking. Adapted from @Maitrot2017.
::: {#appbtbl-validity-internal}
| Method | Ranking |
| --- | --- |
| --- | --- |
| ordinary least squares & fixed-effects | 2.0 |
| discontinuity matching | 3.0 |
| difference in difference (& triple difference) | 3.0 |