In a presentation, I recently argued that tenure standards should shift - to focus on quality & not the quantity of papers.
Some in my audience looked surprised.
Why?
Because when compared to the 1990s, when one had to distribute paper surveys, run subjects in a lab, or manually assemble archival data in a library, many scholars have greater access to data than ever before.
Online panel companies like Qualtrics or Prolific have made it easier to survey respondents or conduct online experiments.
Access to archival sources via private sources such as WRDS or public sources like the US Census has made it easier to secure data.
APIs have made it easier for researchers with a bit of technical aptitude to secure & process unique datasets.
NLP & other tools have made analyzing qualitative & text-based data faster.
Against the backdrop of greater access to data via online panel companies or archival sources, one might mistakenly think it is easier to publish papers.
This would be a mistake.
This would assume that the primary rate delimiter of publication is access to data.
Data is just a tiny part of the publication process.
Beyond data, one must have (1) a great idea, (2) a good research design, & (3) a robust set of contributions.
Access to data does not guarantee faster great idea generation, higher-quality research design, or that a well-executed design will yield vital contributions.
Also, along with data growing more accessible, I have noticed over the past 25 years of academic life that editors & review panels seem to demand more rigor & relevance in published papers.
Further, as data has grown more accessible, I have noticed that the demand for unique data & actual performance data has escalated - requiring more time to design studies & to negotiate the travails of review boards or access to industry data.
So, early career researchers are caught in a buzzsaw.
On the one hand, given that data seems easier to get, tenure expectations have escalated - with uni's demanding more high-quality publications than ever before.
On the other hand, the demands for data, the design of data collection, & the analysis of data that leads to contributions have escalated at a rate that far outpaces the speed with which one can reasonably expect an early career researcher to work.
Beyond data, more PhDs are being granted, by more schools, as graduate programs are scaled up to support the demand for more funded research & low-cost teaching assistants.
So, there is more competition among early-career researchers for a static pool of high-paid (e.g., tenure-track) or lower-paid jobs (e.g., non-tenure track) that demand more research & higher quality teaching.
Frankly, early-career researchers need help.
Uni's demands for quantity have created a no-win situation.
There are not enough hours in the day to meet these demands.
How to solve the problem? More tomorrow.
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