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Notes 04.06

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lindsey edited this page Apr 7, 2023 · 4 revisions

parts of papers we’d pull out:

  • Authors - Affiliations
  • RQs
  • Methods
  • Sources
  • Conclusions / Findings / Implications

valences:

  • Positive claim
  • Neutral claim - eg mislove paper -
  • Negative claim

How did you obtain it?

  1. Company data -> direct agreement with company -> public agreement -> city state fed (eg. NYC) -> API
  2. Human subject - survey
  3. Gov -> census -> tax

other criteria to consider

  • temporal dimension of companies that started and close since 2014/2015

    • Uber Lyft Wingz ….
    • what were the most popular gig companies in 2014 , 2015, etc….
    • we may see an effect on frequency of publications when uber / lfyt other companies just showed up in places

  • global versus US

  • social/cultural contexts: eg. research coming out from a R1 Western university

  • difference of claims made within a country between urban versus rural areas (what's - in a city may be + in a less densely populated area)

next steps

  • come up with the sets of countries
  • look up the 15 largest tnc / dnc operating in that place
  • and include those companies in the queries (uber OR lyft OR deliveroo…)
  • then flter by incl /excl criteria
  • for next week - 04/13:
      1. articulate descriptive questions about our RQs
      1. brainstorm: formulate hypotheses - what do we imagine relationships to be
      1. test research queries in Web of Science and document query parameters in issues -- LS will come up with query facets

top rated gig work platforms as of 2022-2023

“most popular gig companies in X”

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