diff --git a/02-data/supplementary/terms_inequality.csv b/02-data/supplementary/terms_inequality.csv new file mode 100644 index 0000000..c1b5183 --- /dev/null +++ b/02-data/supplementary/terms_inequality.csv @@ -0,0 +1,21 @@ +General,Vertical,Horizontal +inequality,income,identity +barrier,Palma ratio [@DFI2023],demographic +advantaged,Gini coefficient [@DFI2023],gender +disadvantaged,Log deviation,colour +discriminated,Theil,beliefs +disparity,Atkinson,racial +horizontal inequality,class [@Kalasa2021],ethnic +vertical inequality,fertility [@Kalasa2021],migrant +,bottom percentile,spatial +,top percentile,rural +,,urban +,,mega-cities +,,small cities +,,peripheral cities +,,age +,,nationality +,,ethnicity +,,health status +,,disability +,,characteristics diff --git a/02-data/supplementary/terms_policy.csv b/02-data/supplementary/terms_policy.csv new file mode 100644 index 0000000..a08d208 --- /dev/null +++ b/02-data/supplementary/terms_policy.csv @@ -0,0 +1,16 @@ +General,Institutional,Structural,Agency +intervention,support for childcare [@Perez2022],cash benefits,credit programs [@Perez2022] +policy,labour rights,services in kind,career guidance +participation,minimum wage,green transition,vocational guidance [@Nevala2015] +targeting/targeted,collective bargaining,infrastructure,vocational counselling [@Nevala2015] +distributive,business sustainability promotion,digital infrastructure,counteracting of stereotypes +redistributive,work-life balance promotion,quality of education,commuting subsidies [@Perez2022] +,equal pay for work of equal value,public service improvement,housing mobility programs [@Perez2022] +,removal of (discriminatory) law,lowering of gender segregation,encouraging re-situation/migration [@Perez2022] +,law reformation,price stability intervention,encouraging self-advocacy [@Nevala2015] +,social dialogue,extended social protection scheme,cognitive behavioural therapy [@Lettieri2017] +,guaranteed income [@Perez2022],comprehensive social protection,computer-assisted therapy [@Lettieri2017] +,universal basic income [@Perez2022],sustainable social protection,work organization [@Nevala2015] +,provision of living wage [@Perez2022],supported employment [@Lettieri2017],special transportation [@Nevala2015] +,maternity leave [@Chang2021],"vocational rehabilitation [@Silvaggi2020, @Lettieri2017]",collective action +,,unionization, diff --git a/02-data/supplementary/terms_wow.csv b/02-data/supplementary/terms_wow.csv new file mode 100644 index 0000000..ba2bfd4 --- /dev/null +++ b/02-data/supplementary/terms_wow.csv @@ -0,0 +1,13 @@ +General,Forms of work,Labour market outcomes +work,own-use,employment outcomes +labour,employment,labour rights +production of goods,unpaid trainee,equality of opportunity +provision of services,volunteer,equality of outcome +own-use,other work activities,labour force participation [@Pinto2021] +use by others,wage-employed,labour force exit [@Silvaggi2020] +of working age,self-employed,job quality [@Finlay2021] +for pay,formal work,career advancement [@Finlay2021] +for profit,informal work,hours worked [@Finlay2021] +remuneration,domestic work,wage +market transactions,care work,salary +,unpaid work,return to work [@Silvaggi2020] diff --git a/notes.md b/notes.md index de28a60..4a3a3dd 100644 --- a/notes.md +++ b/notes.md @@ -457,6 +457,9 @@ to extraction metadata sheet. ## Search Term clusters +These lists have been used to create data-driven term cluster files in the supplementary data directory. +The lists have been kept here for historic documentation but should not be used for up-to-date term changes, use the csv files instead. + ### World-of-work cluster - ILO: diff --git a/scoping_review.qmd b/scoping_review.qmd index 48c21ee..f555193 100644 --- a/scoping_review.qmd +++ b/scoping_review.qmd @@ -323,52 +323,8 @@ with the search query requiring a term from the general column and one other col ```{python} #| label: tbl-wow-terms #| tbl-cap: World of work term cluster -wow_terms_cluster = { - "General": pd.Series([ - "work", - "labour", - "production of goods", - "provision of services", - "own-use", - "use by others", - "of working age", - "for pay", - "for profit", - "remuneration", - "market transactions" - ]), - "Forms of work": pd.Series([ - "own-use", - "employment", - "unpaid trainee", - "volunteer", - "other work activities", - "wage-employed", - "self-employed", - "formal work", - "informal work", - "domestic work", - "care work", - "unpaid work", - ]), - "Labour market outcomes": pd.Series([ - "employment outcomes", - "labour rights", - "equality of opportunity", - "equality of outcome", - "labour force participation [@Pinto2021]", - "labour force exit [@Silvaggi2020]", - "job quality [@Finlay2021]", - "career advancement [@Finlay2021]", - "hours worked [@Finlay2021]", - "wage", - "salary", - "return to work [@Silvaggi2020]", - ]) -} - -df = pd.DataFrame(wow_terms_cluster) -md(tabulate(df.fillna(""), headers=[wow_terms_cluster.keys()], showindex=False, tablefmt="grid")) +terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv") +md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid")) ``` The world of work cluster, like the inequality and policy intervention clusters below, 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, @@ -383,69 +339,10 @@ For the database query, a single term from the general category is required to b ```{python} #| label: tbl-intervention-terms #| tbl-cap: Policy intervention term cluster -policy_terms_cluster = { - "General" : pd.Series([ - "intervention", - "policy", - "participation", - "targeting/targeted", - "distributive", - "redistributive", -]), - "Institutional" : pd.Series([ - "support for childcare [@Perez2022]", - "labour rights", - "minimum wage", - "collective bargaining", - "business sustainability promotion", - "work-life balance promotion", - "equal pay for work of equal value", - "removal of (discriminatory) law", - "law reformation", - "social dialogue", - "guaranteed income [@Perez2022]", - "universal basic income [@Perez2022]", - "provision of living wage [@Perez2022]", - "maternity leave [@Chang2021]", -]), - "Structural" : pd.Series([ - "cash benefits", - "services in kind", - "green transition", - "infrastructure", - "digital infrastructure", - "quality of education", - "public service improvement", - "lowering of gender segregation", - "price stability intervention", - "extended social protection scheme", - "comprehensive social protection", - "sustainable social protection", - "supported employment [@Lettieri2017]", - "vocational rehabilitation [@Silvaggi2020, @Lettieri2017]", - "unionization", - ]), - "Agency" : pd.Series([ - "credit programs [@Perez2022]", - "career guidance", - "vocational guidance [@Nevala2015]", - "vocational counselling [@Nevala2015]", - "counteracting of stereotypes", - "commuting subsidies [@Perez2022]", - "housing mobility programs [@Perez2022]", - "encouraging re-situation/migration [@Perez2022]", - "encouraging self-advocacy [@Nevala2015]", - "cognitive behavioural therapy [@Lettieri2017]", - "computer-assisted therapy [@Lettieri2017]", - "work organization [@Nevala2015]", - "special transportation [@Nevala2015]", - "collective action", -]) -} +terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv") # different headers to include 'social norms' headers = ["General", "Institutional", "Structural", "Agency & social norms"] -df = pd.DataFrame(policy_terms_cluster) -md(tabulate(df.fillna(""), headers=headers, showindex=False, tablefmt="grid")) +md(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid")) ``` Lastly, the inequality cluster is once again made up of a general term describing inequality which has to form part of the query results, as well as at least one term describing a specific vertical or horizontal inequality, @@ -454,55 +351,8 @@ as seen in @tbl-inequality-terms. ```{python} #| label: tbl-inequality-terms #| tbl-cap: Inequality term cluster -inequality_terms_cluster = { - "General": pd.Series([ - "inequality", - "barrier", - "advantaged", - "disadvantaged", - "discriminated", - "disparity", - "horizontal inequality", - "vertical inequality", - ]), - "Vertical": pd.Series([ - "income", - "Palma ratio [@DFI2023]", - "Gini coefficient [@DFI2023]", - "Log deviation", - "Theil", - "Atkinson", - "class [@Kalasa2021]", - "fertility [@Kalasa2021]", - "bottom percentile", - "top percentile" - ]), - "Horizontal": pd.Series([ - "identity", - "demographic", - "gender", - "colour", - "beliefs", - "racial", - "ethnic", - "migrant", - "spatial", - "rural", - "urban", - "mega-cities", - "small cities", - "peripheral cities", - "age", - "nationality", - "ethnicity", - "health status", - "disability", - "characteristics", - ]) -} - -df = pd.DataFrame(inequality_terms_cluster) -md(tabulate(df.fillna(""), headers=inequality_terms_cluster.keys(), showindex=False, tablefmt="grid")) +terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv") +md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid")) ``` A general as well as category-specific term from each cluster will be required, using a intersection merge (Boolean 'AND'),