Whose Crisis? Reading Data Across Caste, Class, Gender, Religion and more

I co-hosted a podcast around labour and organizing across various themes and intersections and that has led me to see how important it is to continue having conversations about the labour that goes behind keeping a city, office, country going and visibilizing our similarities and differences with understanding will help us come together stronger.

The Central Question we will be grappling with in the dialogue is: How do our intersecting identities and lived experiences change what we see as "the story" in labour data; and how can we build understanding across our differences rather than assume the worst?

Who is this dialogue for?

Practitioners, researchers, and organizers who work with labour data and community stories or anyone curious on this subject.

This includes people who've felt invisible in dominant narratives, those who've realized their privilege shaped their interpretations, mixed roles from different contexts (Global South/North, formal/informal sectors), and anyone grappling with how their position shapes what they notice or miss in datasets.

Importantly: this is for people willing to be vulnerable about their blind spots.

Why is this conversation needed now?

The urgency: We're drowning in data about labour "crises"; gig economy exploitation, care work invisibility, climate migration, pandemic impacts. But whose crisis gets centered? When we see a headline about "essential workers," who do we picture? When data shows "unemployment rising," whose unemployment matters most to us?

In an information-saturated world, we increasingly filter by suspicion; assuming bad faith, reading for what confirms our priors. But labour data is always incomplete by design: it reflects whose work gets counted, whose struggles get named, whose bodies are rendered legible to institutions. Your podcast demonstrated this: the same city looks entirely different when viewed through the eyes of sex workers vs. gig workers vs. domestic workers.

The tension: We need numbers for advocacy (to prove harm, demand resources), but numbers also flatten lived experience. And when we bring our full selves to reading data; our caste position, our gender, our class mobility, our religious context, we see different patterns, different urgencies, different villains and heroes.

This conversation is needed because intersectional literacy is a practice, not a position. It requires us to slow down, compare notes, and ask: "What am I missing because of where I stand?" In a moment where social media rewards hot takes and certainty, this dialogue creates space for collective sense-making across difference.

What perspectives or experiences should be represented?

• People who work with labour data professionally AND people whose labour is the data
• Participants who've experienced invisibility in datasets (domestic workers, informal sector, care workers)
• People across caste, class, gender, religious positions; the goal is productive friction, not comfortable agreement
• Organizers who've had to translate community experiences into "legible" data for advocacy
• Data practitioners who've realized their defaults (what they assume, what they overlook) are shaped by privilege
• Participants from contexts where certain intersections are hypervisible to compare what gets counted where

How will you structure the conversation? (90 minutes)

[0-10 min] Opening: The Same Numbers, Different Stories

Present 3-4 data points about labour (e.g., "gig worker earnings dropped 30%", "essential workers faced highest COVID rates", "care work accounts for X% of unpaid labour")

Quick popcorn round: "When you hear this statistic, whose face do you see? What's your gut reaction anger? sadness? 'I knew it'?"

Make visible: we're all reading the same numbers differently based on our lived experience

[10-25 min] Small Group: Reading for What's Missing (groups of 4)

Each group gets a simple labour dataset or visualization (platform workers, domestic workers, etc.)

Prompt: "Read this data through ONE intersectional lens:

Group A: Read for gender Group B: Read for caste/class position Group C: Read for religion/migration status Group D: Read for ALL three simultaneously

What patterns emerge? What questions does this lens force you to ask? What's invisible in this data that would be visible to someone living it?"

[25-40 min] Experience Exchange: When Data Met Reality

Participants pair up (ideally across different positionalities) and share:

"Tell me about a time you realized your reading of labour data was incomplete because of your position" "When has data about your community or your kind of work gotten it deeply wrong? What did they miss?"

[40-60 min] Full Group: Building Intersectional Literacy Together

Groups report back on what they noticed. The patterns are captured on a visible space (whiteboard/digital board):

What became visible when we slowed down and compared lenses? Where did we see the same data point but understand different urgencies? What questions do we need to ask BEFORE we visualize labour data?

Critical turn:

"When we saw different things in the data, what was our first instinct? Suspicion? Dismissal? Curiosity?"

This is where we bring in the urgency around filtering through understanding vs. assuming the worst. In advocacy work, we often treat differing interpretations as bad faith. But intersectional reading means accepting that someone seeing something different isn't lying; they're standing somewhere else.

[60-75 min] Pattern Mining: Principles for Practice

Small groups (new combinations): "Based on what we've learned, what would it look like to design labour data collection and visualization WITH intersectional literacy built in?"

Can generate concrete practices:

Before collecting data, ask: "Who does this definition of 'worker' exclude?" When visualizing, include: "What this data can't tell you..." In presentations, practice: "Here's what I see from my position, what do you see from yours?"

[75-85 min] Synthesis: Commitments, Not Conclusions

Full group harvest:

One thing you'll do differently in your next data project One question you're sitting with (not answering) One person/perspective you want to learn from after today

[85-90 min] Closing Round

Quick popcorn: "Complete this sentence: Intersectional data literacy is..."

What should participants walk away with?

• Discomfort (the productive kind): Recognition of their own blind spots and defaults
• Curiosity over certainty: Questions they'll carry into their next project
• Peer connections: People who see data differently and are willing to learn together
• Concrete practices: Specific prompts and questions to build into their workflow
• Shared language: Ways to talk about positionality and data interpretation without defensiveness
• Hope: That understanding across difference is possible, even in polarized times

Encouraged mindset:

• Willingness to be wrong, to have missed something, to center others' expertise
• Commitment to curiosity over judgment
• Understanding that this is practice—we're all learning