observing natural events, assigning a conventional interpretation
- omens: solar eclipses, tea cup reading
- phrenology: bumps on skull map to interests
- birth chart
clarity from being assigned to a category
observing natural events, assigning a conventional interpretation
clarity from being assigned to a category
I like tarot as a method of fortune-telling because the cards can act as triggers to the most salient parts of your consciousness, structuring them into a narrative of your existing desires, perhaps previously unexpressed. These unearthed desires may henceforth direct your intentions in its direction, thus becoming a self-fulfilling prophecy.
I wanted my oracle deck to play with this idea of shifting mental states to produce new interpretations and therefore (hopefully) clarity. When learning about cleromancy last week, I especially loved the medieval decks that had silly poems accompanying each card. Personally, reading poetry transports me wholly to different emotional states, and I thought it would be interesting to have a deck where the cards put you into a different headspace from which to interpret and reframe your query.
This may or may not have partially been an excuse to reread my favorite books of poetry… but I think it worked out well enough. My oracle deck contains three suits: Before, Betwixt, and Beyond. These suits emerged organically while I was considering my poetry collection and the emotional themes of each author. Li-Young Lee, whose poems wrestle with memory and the reemergence of past trauma in the present, is my “Before”; Mary Oliver, whose poetry reveres nature and exalts the present moment is my “Betwixt”; and Maya Angelou, who transcended hardship/racism with unfailing optimism and strength, is my “Beyond”. Reading the works of these authors leaves me reflective of the past, grateful for the present, and excited for the future, respectively.
So I combed through a book per author, transcribing poems that emotionally resonated with me into a json file:
Then, I wrote a simple python script that received an input (your query), and chose a random poem for you to meditate on. The lines print one at a time, with pauses after each, to make it a slower, unfurling experience.
Null Hypothesis Significance Testing (p value approach)
Causal and associative hypotheses
All known peoples on earth have practised some form of divination. It has had a critical role in the classical world, ancient Egypt and the Middle East, in the Americas, India, Tibet, Mongolia, Japan, China, Korea and Africa (Loewe and Blacker 1981; Peek 1991).
Over the years, many so-called inductive or rational forms of divination have been compared with Western scientific techniques.
They suggest that in Orisha ceremonies, certain distinguishing drum rhythms and oriki chants are used to attract particular energies, create certain moods, and evoke certain responses.
This alternative way draws its knowledge from “women’s ways of knowing,” from intuitive thought, from dreams, from nature, from the deep recesses of the human psyche. This way of knowing is performative in nature, rich in symbol, ritual, and metaphor, evoking responses that lie deep within the human psyche. For many, it all started with divination as a
sacred compass locating self. For others it started with the rhythm of the drums, the lure of the dance, the transforming experience of symbolic interaction with an unseen, unknown, other dimension of power, the ritualistic replenishing of the primal life force ashe, or the awesome realization that “Words uttered in a particular sequence, rhythm, and tone can bring a rock to ‘action,’ cause rain to fall, or heal a sick person a hundred miles away” (Teish 1988, 62)
Inspired by my bemusement over Benjamin Burroughs’ seemingly contradictory claim that Facebook is at once “less about networking and more about broadcasting” and “a salient example of digital third places”, I decided to challenge it—that is, my own bemusement—with this week’s ritual.
I haven’t been active on social media for a few years now. My usage peaked after I moved to NY for my first job: I was a naive, introverted Midwestern girl, barely 22-years-old, newly single, with no friends and no social skills outside of the classroom; I naturally turned to Facebook and Instagram for human connection. Becoming reliant on social media basically means developing a hardcore addiction to its fickle yet relentlessly available stream of validation. It was devastating if my posts went ignored; I deleted the ones that accumulated less than double-digit likes, and continued posting more of what had been celebrated previously. The act of posting itself came with its own sick thrill (a mix of terror and pride), which could be reproduced to a degree by compulsively checking back in on the post at every opportunity, watching the “likes” count rise (or not). “Broadcasting” really is the best word for what I was doing: broadcasting evidence of my existence to everyone I’d ever known, then hoping for collective approval in return. I was like a boring, self-absorbed comedian with severe stage fright, returning to the mic day after day, desperate to trick everyone in the audience into laughing as if it was the only way I could feel good about myself.
So I was a pretty dysfunctional user, to say the least, and unfortunately didn’t realize how detrimental this was to my mental health until it was too late. That’s the thing about depression: even when it’s spiraled to a point where you hate everything about yourself and suicidal ideation is the only thing playing on every channel, at the end of the day, the truth is that you are obsessed with yourself. And social media is truly the best and worst outlet for the self-obsessed: best, because it’s essentially a big ol’ stage chanting your name; worst, because the audience never laughs hard enough.
Four years later, I set out to see if I could use Facebook completely differently—as a “third space” rather than a tool for broadcasting. Sure, Messenger isn’t exactly made for broadcasting, but it’s also not what makes Facebook, Facebook—it’s just a knockoff of the American Online paradigm. What I wanted to see is if I could somehow co-opt the Newsfeed into a third space; the broadcasting feature would just help to bring people into it.
My plan was simple: a collective gratitude practice. Every night, I would post a list of what I was grateful for that day, and beseech readers to do the same in the comments. I thought this would be an effective exercise because
Before submitting the first post, I had a moment of complete regret/panic for what I was about to do; it felt like I was about to text That Greenpoint Guy (you know, the unkempt but witty musician who only told you he loved you after the sun set and he’d had a couple to drink, which was somehow enough to keep you emotionally attached for six years. Yes I made terrible decisions in my early twenties) and fall into an emotional, codependent black hole all over again.
But then I just hit “Share”, for science.
Things of note from the fallout:
It’s for these reasons that I started to scheme a different approach: same exercise, but through Messenger. I know, I know, but the broadcast-y nature of the Newsfeed seemed to be a major pain point in the way of engagement. Also, posts seem to get completely buried under ads (when did fb start serving so many ads?). I hypothesized that most, if not all, of my friends would humor me with their participation if I contacted them through a direct message. It would also afford for longer conversations, which are tedious and thus entirely avoided in comments sections.
Plus, the public interest in this experiment fizzled out nearly immediately:
Note the dreaded SINGLE DIGIT “LIKE” COUNT. Yeah, I was pretty done with this daily act of public humiliation.
So instead, I wrote a python script that collects moments of gratitude throughout the day in a json file; at 10:30pm, it launches an npm package called Messer (a command-line interface for FB Messenger that required me to author a few issues before it was working properly for my purposes), chooses a friend at random, and sends them a message requesting a shared gratitude practice.
Messer botched the first message it attempted to send (hence the bug reports), but it still led to a really wonderful conversation with an old co-worker of mine—from my very first job— that I hadn’t spoken to in four or five years. We caught up, shared our good things from the weekend, and at the end even made plans to meet up. It was so sweet and made me really happy—such a conversation would have never occurred if I’d kept up with my daily gratitude broadcast. In fact, he was one of my friends who’d “liked” my posts, but didn’t comment—even though he said my post made him miss me (which I like to think confirms my foregoing theories):
As much as I loved the idea of a mass gratitude practice, where everyone could benefit and feel a little closer to each other, this ended up being much more fulfilling for me.
Here’s the message it’s currently sending:
As my soft opening for this ritual, I’m only drawing from a pool of friends who had “liked” or commented on my gratitude posts. Later, I may expand it to my entire friends list.
assignment: find an artist and find a contrast between your and their rituals; 7 hours
Religious Exoticism and the Logics of Bricolage
“Individual parts of mathematical statistics must look for their justification toward either data analysis or pure mathematics.”
Large parts of data analysis are (but as a whole is larger and more varied than):
How can new data analysis be initiated?
data analysis is a science because it has 1) intellectual content, 2) organization into an understandable form, and 3) reliable upon the test of experience as the ultimate standard of validity
(Mathematics is not a science: standard of validity is an agreed-upon logical consistency and provability)
Data analysis, and the parts of statistics which adhere to it, must…take on the characteristics of science rather than those of mathematics:
data analysis is intrinsically an empirical science
data analysis must look to a very heavy emphasis on judgement:
a scientist’s actions are guided, not determined, by what has been derived from theory of established by experiment
scientists know that they will sometimes be wrong; they try not to err too often, but they accept some insecurity as the price of wider scope; data analysts must do the same
“Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.” Data analysis must progress by approximate answers, at best, since its knowledge of what the problem really is will at best be approximate.