aac958ba8f
fix(data): Fix design for Kuriyama
2023-12-18 15:26:39 +01:00
1ea9b86174
feat(data): Extract Poppen2017
2023-12-18 15:23:59 +01:00
ea338908d2
feat(data): Extract Wang2016
2023-12-18 14:52:32 +01:00
3eb989dbcf
feat(data): Extract Pi2016
2023-12-18 14:52:17 +01:00
e4030581d4
fix(data): Fix education intervention category
2023-12-18 13:28:26 +01:00
2826ac894d
feat(data): Extract Liyanaarachchi2016
2023-12-18 11:11:13 +01:00
5e2a8c6c3b
chore(data): Update library with PDF files
2023-12-18 11:10:50 +01:00
daee236eac
feat(data): Extract Mun2018
2023-12-14 19:08:25 +01:00
b0308600a2
fix(data): Rename maternity leave to paid leave
...
To reflect a) the possibility of gender-less paid leave and b) the child-care
not just being maternal but possible for other relations.
2023-12-14 19:08:09 +01:00
f51505a57c
chore(data): Sort out irrelevant study
2023-12-14 19:07:05 +01:00
7241b62efe
fix(code): Load data script from anywhere
...
Allow loading the script both through quarto using full absolute path and through
the command line using (I believe) a relative path.
2023-12-14 18:08:13 +01:00
38099c3358
feat(code): Output data as csv to stdout on script run
...
When running data python script (`src/data.py`) directly through the command line,
we now use pandas to output the collected data directly as a csv to stdout.
Can then be redirected to e.g. a file to save the data in csv format.
2023-12-14 18:06:39 +01:00
73b96a757a
feat(data): Extract Militaru2019
2023-12-14 17:42:41 +01:00
b594233d01
feat(data): Extract Davies2022
2023-12-14 16:33:21 +01:00
c3369e83e7
chore(script): Change methodology and inequalities headlines
2023-12-13 21:15:00 +01:00
2fc730812f
feat(data): Extract Khan2021
2023-12-13 21:14:31 +01:00
124e520835
chore(data): Sort out full-text studies
2023-12-13 20:48:28 +01:00
f63cf44f74
feat(data): Extract Standing2015
2023-12-13 20:02:04 +01:00
30b649f31f
chore(script): De-indent intervention headlines
...
Until we have thematic areas, de-indent the individual headlines for the time being.
2023-12-13 15:46:21 +01:00
84f1d64492
chore(script): Update TODOs and FIXMEs in script
2023-12-13 15:04:19 +01:00
97f6214932
chore(script): Refactor synthesis toward interventions
...
Refactor to start breakdown from interventions and then move towards
individual categories and inequality breakdowns within the sections.
It should follow intervention -> inequality/region logic.
2023-12-13 14:44:08 +01:00
8c2c83cd9a
chore(script): Include superseded in duplicate count
...
Included studies marked as 'out::superseded' in the duplication removal step.
2023-12-13 14:42:34 +01:00
c35b205345
chore(data): Update library and country separation
2023-12-13 14:40:16 +01:00
9789936712
feat(data): Extract Xu2021
2023-12-13 14:39:37 +01:00
cf4c39a3c8
feat(notes): Add simple measure documentation
...
From documents in DB, requires more attention and revision.
2023-12-13 13:50:41 +01:00
6e4f19ac3f
!fix(data): Fix measures collected
...
Fixed the measures and directions collected to use Gini, Atkinson, .. measures or
absolute employment, poverty, etc.
2023-12-13 13:50:40 +01:00
50409bb9d3
fix(data): Change Debowicz2014 to simulation design
2023-12-12 21:37:38 +01:00
d8a018b7b0
feat(notes): Add meeting notes December
2023-12-12 20:12:20 +01:00
7db335a479
feat(data): Extract Cieplinski2021
2023-12-12 20:08:02 +01:00
874e708112
chore(data): Sort out irrelevant study
2023-12-12 20:07:45 +01:00
26afa3b8ea
fix(script): Add missing Rosen2014 annotation
2023-12-12 08:48:25 +01:00
30d059d68b
chore(script): Imply regional/findings breakdowns
2023-12-12 08:46:59 +01:00
9855256b00
feat(notes): Add intermittent findings and progress
...
Created small up-to-date quick glance document for findings and
data set.
2023-12-11 17:17:43 +01:00
b5e467e016
feat(code): Add examples of list handling notebook
...
Extracts interventions/inequalities and explodes them for value counts.
2023-12-11 17:17:42 +01:00
85497854c1
feat(data): Extract Adams2015
2023-12-11 17:17:42 +01:00
6041d00d8b
chore(data): Categorize further studies for tags
...
Tag studies for income, gender, racial and ethnicity inequalities
based on title keywords.
2023-12-11 17:10:31 +01:00
6104f2d274
feat(data): Extract Rosen2014
2023-12-11 09:49:46 +01:00
b09e65476b
fix(data): Unify spelling Emigh2018 intervention
2023-12-10 19:49:31 +01:00
ef06e4bb88
chore(data): Sort out Dumas2018
2023-12-10 19:29:47 +01:00
af2df5736c
chore(script): Refactor pandas data ingestion
...
Load data at top of file, then use chained methods for
visualizations.
2023-12-10 19:29:29 +01:00
0e29a3332c
feat(data): Extract Emigh2018
2023-12-10 19:29:28 +01:00
ca7eab92d3
chore(script): Refactor pandas data ingestion
...
Load data at top of file, then use chained methods for
visualizations.
2023-12-10 18:00:27 +01:00
1ba2daeacd
feat(data): Extract Emigh2018
2023-12-10 17:59:19 +01:00
50ce8b6310
feat(data): Extract Shepherd-Banigan2021
2023-12-10 17:59:17 +01:00
85c6340b5c
fix(data): Remove 'poverty' as inequality category
...
Poverty is a status but not directly inequality
2023-12-10 17:57:50 +01:00
7258915ef1
feat(script): Extract Gates2000
2023-12-10 11:33:56 +01:00
efd6e285c8
feat(script): Extract Hardoy2015
2023-12-10 11:04:19 +01:00
8e7f99b20d
chore(script): Refactor dataframe loading code
...
Improved readability of dataframe loading, used improved chaining
and some list comprehension to make it much less messy.
2023-12-09 23:46:21 +01:00
3f05283f6d
chore(script): Refactor screening flowchart calculations
...
Made it much clearer and simpler how numbers are calculated for the
screening flowchart. Now we just keep the actual numbers in memory
and not a copy of the whole bibtex library for each calculation step.
Also renamed the bibtex variables to be more sane, `bib_sample_raw_db`
(the raw, unaltered sample returned from querying the databases), and
`bib_sample` for our working sample including database queries and
snowballing studies but already deduplicated (since we can't keep
an unduplicated version on Zotero).
2023-12-09 22:06:05 +01:00
708fa90d29
chore(script): Refactor file top imports
2023-12-09 21:54:26 +01:00
