Mismatching epistemological hierarchies explain perhaps a quarter of #debates that end up unproductive or even bitter for me.

I wouldn’t dare define my Pyramid of Sources of #Truth without giving it some careful thought first, but… a first approximation:

💡 Quantitative data or stats that are either common knowledge or directly accessible to most people (eg, no. of seats in the Spanish Parliament, minimum height of all outside doors in Granada Cathedral).
⬇️
💡 Quantitative data or stats issued by sources commonly understood to be authoritative and unbiased (eg, average size of all dentist’s offices in Norway according to the Ministry of Health).
⬇️
💡 Old, peer-reviewed, published meta-analyses that are accessible (eg via #SciHub) to most people.
⬇️
💡 Old, peer-reviewed, published meta-analyses.
⬇️
💡 Old, peer-reviewed, published papers.
⬇️
💡 Peer-reviewed, published papers.
⬇️
💡 Peer-reviewed papers.
⬇️
💡 Reports, surveys, research, whitepapers, polls — with a wild degree of confidence, depending on the particulars (see parameters below).
⬇️
💡 Published books, theatrical documentaries.
⬇️
💡 Self-published books, self-produced documentaries.
⬇️
💡 Long blog posts with links to secondary sources.
⬇️
💡 Newspaper articles fall somewhere around here.
⬇️
💡 Blog posts.
⬇️
💡 Online videos, tweets, screenshots, photos, voice messages, viral clips, copied-and-pasted quotes, hearsay, anecdote, feeling, hunch, rumour, revelation.

In all cases, the bigger these parameters, the higher a particular #sourceOfTruth moves up my hierarchy:

🔺 Sample size.
🔺 No. ot times result has been reproduced.
🔺 Boringness of result (ie, how common-sensical and unsurprising it seems to the average person).
🔺 Parsimoniousness (ie, simplicity) of interpretations given for result.
🔺 Awkwardness of result for parties involved (ie, how inconvenient it is for the interests of authors themselves).