Situating Women in Urban Slums: Socio-economic Dataset from Slums in Lucknow, India

Tracking #: 800-1780

Authors:

	Name	ORCID
	anushree nagpal	https://orcid.org/0000-0002-6366-7430
	abhra singh	https://orcid.org/0000-0002-8489-3605
	Masood Ahsan Siddiqui	https://orcid.org/0000-0002-7153-9156
	Mohammad Hashim	https://orcid.org/0000-0003-0231-0697

Responsible editor:

Victor de Boer

Submission Type:

Resource Paper

Abstract:

The present study provides field based socio-economic data of women slum dwellers in the city of Lucknow, the administrative capital of Uttar Pradesh, India. Being one of the most developed cities in Uttar Pradesh, Lucknow attracts a lot of migrants most of whom come for better economic opportunities and settle in the low-income neighbourhood of the city. Consequently, the number and population of slum colonies has grown simultaneously. In the present times, an increasing number of women migrate independently and are the principal wage earners for themselves and their families. However, since women come with limited job skills and other limited resources, many of them end up in urban slums wherein they remain at a disadvantage in terms of equitable access to work and other aspects compared to their male counterparts. In order to draw a holistic picture of the status of female slum dwellers, an exhaustive socio-economic field survey (2020-21) for 240 women respondents, across 21 slum colonies, was carried out, collecting data on 121 diverse aspects. This high granularity socio economic dataset can be used for carrying out interdisciplinary research as well as formulation and implementation of slum development and urban poverty alleviation programmes.

Manuscript:

ds-paper-800.docx

Supplementary Files (optional):

ds-supplementary-800-1289.docx

ds-supplementary-800-1290.docx

Data repository URLs:

https://data.mendeley.com/datasets/2g6y7g24tw/1

Date of Submission:

Monday, March 4, 2024

Date of Decision:

Monday, May 13, 2024

Nanopublication URLs:

Decision:

Undecided

Solicited Reviews:

Review #1 submitted on 05/Apr/2024

By Kousar Aslam ORCID logo

https://orcid.org/0000-0003-2474-0188

Review Details

Reviewer has chosen not to be Anonymous

Overall Impression: Average
Suggested Decision: Reject
Technical Quality of the paper: Good
Presentation: Good
Reviewer`s confidence: Medium
Significance: Moderate significance
Background: Reasonable
Novelty: Limited novelty
Data availability: All used and produced data (if any) are FAIR and openly available in established data repositories
Length of the manuscript: The authors need to elaborate more on certain aspects and the manuscript should therefore be extended (if the general length limit is already reached, I urge the editor to allow for an exception)

Summary of paper in a few sentences:

The paper presents a socio-economic study to analyze the status of women slum dwellers in one of the most developed cities in Uttar Pardesh. The data is collected through an extensive survey, covering 121 aspects, for 240 women across 21 slum colonies. The data is fine grained and may be used for further interdisciplinary research (needs to be clarified in the paper) or for providing guidelines for government and policy makers.

Reasons to accept:

Although the work done is comprehensive, I do not agree with accepting the paper in its current form.

Reasons to reject:

- The paper lacks a relevance for this journal. I do acknowledge the data is valuable but it is more useful for social sciences, the scientific relevance of data science is not well presented. Is the paper suggesting to use this dataset to solve problems in slums through some technological solutions? The paper needs to clarify this point.

- The paper does not contain a discussion regarding the insights from data.

- There are no limitations discussed for the work performed. Such a work can be impacted by a self bias, background knowledge and other human aspects between interviewer and interviewee.

- The paper ends abruptly without concluding, lacking a proper take away message for the reader.

Nanopublication comments:

Further comments:

Review #2 submitted on 03/May/2024

Review Details

Reviewer has chosen to be Anonymous

Overall Impression: Average
Suggested Decision: Undecided
Technical Quality of the paper: Average
Presentation: Average
Reviewer`s confidence: Medium
Significance: Moderate significance
Background: Reasonable
Novelty: Limited novelty
Data availability: All used and produced data (if any) are FAIR and openly available in established data repositories
Length of the manuscript: The authors need to elaborate more on certain aspects and the manuscript should therefore be extended (if the general length limit is already reached, I urge the editor to allow for an exception)

Summary of paper in a few sentences:

This is an important study addressing a critical gap in our knowledge about slum populations. Specifically, this work has developed a dataset on women in slums, with a specific focus on the Lucknow community in India. This work is following traditional social science principles in data collection and is both value as it is timely given the number of studies on slum populations globally, with sometime little considerations of some of the disparities amount gender.

Reasons to accept:

The data is valuable.

Reasons to reject:

Not being a social scientist, I don't see where the 'data science' comes into play into this word. As someone more on the quantitative side, I would reject this manuscript given the journal's focus. However, I leave this decision up to the Editor.

Nanopublication comments:

Further comments:

Overall
• This is an important study addressing a critical gap in our knowledge about slum populations. Specifically, this work has developed a dataset on women in slums, with a specific focus on the Lucknow community in India. This work is following traditional social science principles in data collection and is both value as it is timely given the number of studies on slum populations globally, with sometime little considerations of some of the disparities amount gender. For the most my comments are quite general, but a main query towards the applicability of this work to this “Data Science” journal without much work/tools being utilised, if any at all. This, of course, is left to the Editor to come to a decision on this. There may be a more suitable journal out there in my view.
• While the study is principally about the curation of a data for use, a map of the location would help. This can be something along the lines of the location (general or absolute) of the slums within respect to Lucknow. For those, including myself, not familiar with the location, this would provide some context. This can be part of a Study Area section where additional contextual details about the area is provided.
• For several variables in the curated dataset, most have NA values. I’m wondering if these are then applicable, or should they be removed altogether.
• I’m missing a summary section following a summary of the data. The paper ends quite abruptly. Even though the focus is on producing a dataset, this is still an academic journal.
• Few additional points follow.

Introduction
• Could be bolstered with additional support from the literature.
• Units are in lakhs – while this is ok for India and similar countries around in terms of readership, it’s less likely that a lot of other countries’ readers will immediately be familiar with this measure. I suggest changing this to a more relatable unit for wider readership.

Methods
• The sentence of the first paragraph seems more fitting towards the end of the Introduction section
• This section is about the value of data really has nothing to do with methodology. In here one would expect to see survey methods etc. I think all of these points fit better within the remit of the Data section, or can be woven into the Introduction section.

Sampling procedure
• “For the selection of wards, initially, the slum colonies were put in descending order of their population size. Arithmetic mean of the whole distribution was then computed which led to its classification into two groups. The mean of each of the two groups further led to creation of four groups with lesser intervals.”
o I’m not sure how the two groups were created based in the mean. Was it one group within one standard deviation from the mean and the second group everything beyond this? A more information is needed, especially since, based on the distribution of values, the mean can be somewhat biased as a measure. Other than this, I think the sampling procedure was fair.

Data collection and survey execution
• I was wondering if there was a specific reason for the data being collected at two different time windows, that i.e., March to May, and August to October? I think some statement should be made here, whether it be due to resource limitations, requirement for support from safai navaks, etc. Or a the very least a statement should be made with respect to changes in slum population across these two time windows so users of the data can understand fitness for their specific uses.
• How were the specific priamry variables decided? One could argue that other data may have been more relevant. We’re these based on specific themes, secondary data availability, etc.?

Seocio demographic parameters
• “The median age of the respondents is 44 years. Majority of the surveyed households (56.67%) are Hindus and in terms of caste, a significant proportion of minority communities were found to be living in the slums.”
o Being unfamiliar with minorities with the caste system, I don’t what this means. Can some examples of minorities castes be provided? Or a link to additional information?
o “…followed by SC and ST communities” – What are SC and ST relating to here?

1 Comment

meta-review by editor

Submitted by Tobias Kuhn on Mon, 05/13/2024 - 09:04

The two reviewers agree that the resource described in the paper is potentially valuable and described in a comprehensible way, but that in its current form, the relevance to the journal is unclear at best. To be accepted in the Data Science journal, the authors would need to make explicit how this resource can be of value to the larger data science community. The authors could do this my providing some examples or use case, preferably supported by data science literature. Additionally, the authors can add a section summarizing the dataset more clearly (following reviewer 2's suggestion) and discuss limitations (reviewer 1). This will also further provide proper data science-related contextualisation for the resource.

Victor de Boer (https://orcid.org/0000-0001-9079-039X)