A hypothetical: a motivational speaker fabricates his rock-bottom story and, when checked, reaches for a storyteller’s license after years of being received as a business mentor. The field you are received in, not the label you claim, sets the standard you answer to.
A hypothetical hearing: the chair’s words are careful and many-caused, and the event still performs a single accusation. That gap is why I started reading what a message says and what its form does as two different things.
A hypothetical: the statement is accurate, grieving, and still functions as bullshit. The reason looks structural, a communications chain built to limit liability that subordinates the truth no matter how much the people in it care.
A map across the Pamamahalang Pilipino essays. The name carries the argument, every Filipino value is dual-use, and scale and structure, not exhortation, decide which face shows.
A synthesis across the AI-and-teaching essays. AI removes the friction of reading, verifying, and prompting, and that friction was doing the teaching, so the work becomes deciding which frictions to protect.
A map across the business-and-society essays. Profit is a byproduct, more-than-profit concedes too much, and whether a business does good is read in its configuration, not its declarations.
A synthesis of the Daily Gospel Insights. Reading each day’s Gospel as a seed for one management insight, and the conviction underneath: that organizations exist for the flourishing of persons.
The theoretical spine. Lonergan supplies insight and authenticity, Archer the morphogenetic mechanism, Bhaskar the stratified depth, and together they keep mechanism and direction apart.
A synthesis of the bullshit-and-verisimilitude essays. The series moves honesty from the true-or-false surface to the disposition underneath, where the line may be real yet hard to verify.
Asking how a business changes the world is asking about its innovation. Innovation recombines existing capacities into a whole with new properties, and which way that recombination points is a moral choice rather than a technical one.
The first post, and a small argument about what an academic should use a blog for.
A worry about Diwa-Kapwa-Loob: if diwa is undone by being claimed, can it ground a management theory at all? It can, because naming a structure and claiming a personal virtue are different acts. The framework checks organizations; it does not certify people.
On the shadow side of Filipino values: the same orientations that build solidarity can be turned, fluently, into extraction.
Purpose-washing is not caused by an organization being explicit about its values. It is caused by the gap between what the bylaws declare and what the configuration actually does. So audit the structure instead of trusting the proclamation.
The difference between a value you declare and a virtue you practice, and the discipline that turns one into the other.
Certain traits cannot survive being claimed. The moment a leader announces humility or servanthood, the announcement re-centers the self and produces the opposite of what it names.
Three familiar explanations for Philippine corruption, and why a scale mismatch explains more than any of them.
A one-letter choice, and the case for an indigenous Filipino management theory built by recovery rather than invention.
Awards from Geneva and a Vogue spread look like vanity until you watch them hold off the quarries. International attention becomes armour around a Philippine watershed, and I find that clever and uneasy at once.
A claim that disruptive innovation is itself a form of blending: existing technology, a new market segment, and a different cost structure, recombined. The disruption label centers whoever got displaced, not the act of creation.
If innovation is recombination, the part that matters is the act of grasping a unity others missed. That grasp, what Lonergan called insight, has conditions, and our diwa is what opens or blocks them.
If innovation is creative blending, the hard part is that a blend can go wrong. Tamang timpla names the sense of proportion the craft requires, and mapagtimpla names the person who has practiced into it.
The standard Filipino word for innovation, makabago, puts the weight on bago, new, and trains us to chase novelty. The real work is malikhaing pagtitimpla, the skill of getting the proportions right.
Varacco is a tech startup to investors and a farmer cooperative to the cabildo. I want to defend that doubleness as competence, and ask whether the demand to just be authentic belongs only to whoever already fits the mould.
The English word innovation means to make new, which hides what innovation actually does. The Filipino phrase malikhaing pagtitimpla, creative blending, names the real act: combining things that already exist into a configuration with properties none of the parts had alone.
Two firms can share an identical mission statement about malasakit and still produce opposite outcomes for the people inside them. The difference is structural, which means dignity can be checked rather than proclaimed.
The more-than-profit language quietly reinstalls profit as the master purpose and treats everything else as a discretionary add-on. The structural case rejects that frame: profit is a survival condition, not the purpose a business exists to serve.
A functioning business is an arrangement that produces dignity and ginhawa no single owner or worker could produce alone. Profit is one output of that arrangement, not the reason it exists.
Most help carries a quiet diagnosis: that the people being helped are broken. I think that diagnosis can produce the very helplessness it points to as proof it was needed.
A coffee enterprise pays farmers up to six times what middlemen offer. It looks irrational until you see the moat it builds, and the assumption that made it look irrational in the first place.
Open the books of a community enterprise and an analyst sees obligations as liabilities. In four Philippine cases, those same ties bought market flexibility, not less of it.
Business education is fluent in proposals and untested in consequence. A student’s offhand remark, and an action research course, made me see what we grade and what we quietly avoid.
I teach action research because it puts students next to a real problem with real people. But many of my students can walk away at the end of twelve weeks, and the people in the community cannot. That asymmetry needs naming, not warm language about partnership.
The story of active learning is usually about students rising up. The harder story is about the teacher giving up the script and absorbing the cost that replaces it: the stamina to keep giving feedback.
We file reflection under soft skills, the part you do after the real work. When I mapped the connections in my students’ action research, the thickest line ran from reflection to well-being.
The series leans on retroduction to tell bullshit from truth-likeness. But a sophisticated bullshitter can counterfeit every sign of good faith, which means the distinction may be real and still unobservable from outside.
Behind every compliance module sits an unstated bet about human nature. The pessimistic bet has a cost: build for distrust and you may produce the very behaviour you were guarding against.
Students keep calling the AI’s reading suggestions too general and Western-centric. They are right, and the reason is built into what an averaging machine is.
The struggle to write a good prompt is the struggle to know your own mind. I wonder whether that friction was the lesson all along.
We worried AI would write the student’s essay. In my research class the more interesting thing happened on the reading side, and a rented understanding is hard to tell from the real thing.
Two of my own positions pull against each other. Truth-agnostic text at scale corrodes our ability to tell true from false, yet I keep telling good actors to flood the same channel with verisimilitude. I have not reconciled them.
Some of my students felt bad using AI on their research. The usual move is to reassure them. I want to take the opposite seriously: maybe the discomfort is the signal worth keeping.
In my business research class, the most diligent way students used AI was to verify their own work. Confirming correctness is a judgment, and the model makes none.
A single true-or-false axis cannot tell honest approximation from bullshit. Three independent questions can, and the second one does most of the work.
The accountability question about AI sits with the human system, not the machine. The same model produces verisimilitude or bullshit depending on who answers for it.
The liar and the bullshitter both have a stance toward truth. A language model has none. It sits below the level where caring is even possible, and that needs a third word.
I used to call honest fabrication ‘good bullshit.’ I was wrong, and the way I was wrong taught me where the real dividing line sits: accountability, not surface facts.
A worked example: take a single audited fact and tell it truthfully across temporal, narrative, emotional, constituency, and cultural frames. The output multiplies; the anchor does not move.
I had assumed honesty meant a few dry restatements while bullshit got to be infinite. That assumption was wrong. A single verified fact radiates unlimited honest expressions, each one anchored to the same truth.
On the platform where most of us argue, bullshit scales for free. Verisimilitude cannot win by posting faster. It wins when reality itself throws off the volume.